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Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An Exploratory Study

Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An... International Journal of Environmental Research and Public Health Article Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An Exploratory Study 1 , ID 2 ID 3 ID Jose Ramon Saura * , Pedro Palos-Sanchez and Miguel Angel Rios Martin Department of Business and Economics, Faculty of Social Sciences and Law, Rey Juan Carlos University, Paseo Artilleros s/n, Madrid 28032, Spain Department of Business Organization, Marketing and Market Research, International University of La Rioja, Av. de la Paz 137, 26006 Logroño, Spain; ppalos@unex.es Department of Financial Economy and Operations Management, Faculty of Economics and Business, University of de Sevilla, Av. de Ramon y Cajal, 1, Sevilla 41004, Spain; rios@us.es * Correspondence: joseramon.saura@urjc.es; Tel: +91-488-80-11 Received: 12 January 2018; Accepted: 15 March 2018; Published: 19 March 2018 Abstract: The object of this exploratory study is to identify the positive, neutral and negative environment factors that affect users who visit Spanish hotels in order to help the hotel managers decide how to improve the quality of the services provided. To carry out the research a Sentiment Analysis was initially performed, grouping the sample of tweets (n = 14459) according to the feelings shown and then a textual analysis was used to identify the key environment factors in these feelings using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). The results of the exploratory study present the key environment factors that affect the users experience when visiting hotels in Spain, such as actions that support local traditions and products, the maintenance of rural areas respecting the local environment and nature, or respecting air quality in the areas where hotels have facilities and offer services. The conclusions of the research can help hotels improve their services and the impact on the environment, as well as improving the visitors experience based on the positive, neutral and negative environment factors which the visitors themselves identified. Keywords: environment; sentiment analysis; Nvivo; twitter; textual analysis 1. Introduction 1.1. Global Overview of Tourism Trends Over the past few years, the use of new technologies has led to changes in the environmental, cultural, economic and social sustainability sectors from how they were at the beginning of the 21st century [1,2]. Tourism and the activities linked to its growth have become a key part of the world economy and have also made local populations suffer the consequences of the impact of these activities on the environment [3]. Activities for tourism provide important amounts of income and jobs, promote knowledge of other cultures and conserve cultural and natural heritage, as well as investing in local infrastructure [4]. These activities provide both economic and social benefits. However, not everything is positive as some styles of tourism and certain recreational activities can lead to the destruction of habitats, the deterioration of the landscape and competition for scarce resources and services (fresh water, territory, energy, wastewater treatment, etc.) [5]. In addition, local populations may lose their traditional way of life as a consequence of these activities and could also become excessively dependent on the income generated by tourism [6]. Int. J. Environ. Res. Public Health 2018, 15, 553; doi:10.3390/ijerph15030553 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2018, 15, 553 2 of 20 The increase in prices associated with tourism can also negatively affect the local population, which runs the risk of losing land, houses, shops and services for the construction of hotels, hostels, rural houses or tourist services [7]. These problems are worsened by the concentration of tourist activity in relatively short holiday periods and in certain limited areas. These areas may also be subject to environmental pressures from other economic activities such as agriculture, fishing, and industrial development or from the increasing resident population. More than other sectors, tourism and recreational activities depend on the quality of the natural and cultural environment for their long-term success. It can be seen therefore, that tourism can affect the natural environment to the point of endangering its own existence. To stop this, it is important to identify the environment-related factors which hotel users detect during their stays at hotels. These users are aware of the problems and the impact of tourism on the environment [8,9]. It is important to draw attention to the fact that the development of new technologies, and especially social networks has caused important changes to the tourism sector [6]. One great change is that people who stay at hotels can easily make comments on the Internet and provide information about their experience and the quality of their visit [9,10]. Like Hung, Kuo and Lin [6] the research of Bifet and Frank [7] analyzed the posts made on social network by companies in one sector. The results of the research show that the tone and narrative used in the post are directly associated to the interest shown in the post by both mobile device and desktop social networks users, which allows for the identification of the key factors which should be used to establish company strategies [11–14]. For some time, social networks and understanding their use has been of interest to researchers in different sectors, such as environmental, cultural, economic and social sustainability. In the work of Kwon [15–17], in which a qualitative approach is taken to determine the attitudes of older people, or the work of Honeycutt et al. [18] in which a textual analysis is performed to determine and understand the short- and long-term impacts in the field of environmental science and public health. 1.2. Literature Review The interest that researchers have in identifying environmental factors which affect the users’ experiences when staying at hotels must also be considered. The interest of researchers in this area is shown in various research papers, such as those of Hussain and Singh [19], in which the research aims to discover the attitudes and behaviors of hotel users concerning sustainability. The work of Suanmali [20] is also concerned with detecting the factors that affect tourists’ satisfaction with the Northern Part of Thailand, using an empirical study relating to the environment. In the work of Kaltenborn et al. [21], tourists’ attitudes are investigated concerning the environmental, social and managerial attributes of the Serengeti National Park, concluding with the identification of factors that affect hotel users concerned about the environmental impact of hotel activities. The work of Bruyere [22] aims to identify rural hotel users’ feelings about the benefits of exploiting rural areas for tourism and then suggest how the management of rural hotels can use these findings. Eliam and Trop [23,24] research users’ opinions on activities that affect the environment, identifying the main points which concern travelers. In order to identify the factors that affect Spanish hotel users during their stay, we compiled a set of factors that affect the environment (see Table 1). It is important to identify the environment factors detected by the users of Spanish hotels as these users are already aware of the problems and the impact of tourism on the environment. Therefore, the experiences and feelings of these users when identifying environment factors of the tourism industry are very valuable. It is also useful to remember that the world tourism sector keeps growing [25]. In 2016, 1235 million tourists traveled to foreign countries, which means some 46 million more than the previous year, according to the World Tourism Organization (WTO) [26,27]. This is the seventh consecutive year of increases. However, the growth rate has slowed down as in 2016 the increase was 3.9%, whereas in Int. J. Environ. Res. Public Health 2018, 15, 553 3 of 20 previous years the rise has been around 4.6%. This means that last year there were 300 million foreign tourists more than in 2008 [28–30]. Table 1. Tourism factors and their impact on the environment. Factors Environmental Impact Accumulation of public Stress for the environment and animals Extreme sports Disturb the fauna Animal feeding Changes in wildlife behavior Diving and snorkeling Damage to sea beds Camping/Picnics Erosion of the soil. Damage to vegetation. Noise. Rubbish Hunting and fishing Reduction of species Off-road driving Destruction of soil and vegetation Noise emission Disturb the animals Climbing, hiking Damage to vegetation Cars Running over animals, pollution, noise “Souvenir” collection Interruption of natural processes Collecting wood Deforestation, destruction of habitats Throwing garbage Deterioration of space and danger to local animal and human health Discharge of waste not suitable for water Water pollution, acidity Construction of facilities Loss and division of habitats Construction of electricity pylons Impact on birds in flight Tourism infrastructure Visual impact on fauna, vegetation and aquatic habitat If we look at 2017 WTO world tourism figures, the number of visitors at destinations all around the world shows that these was a large demand for international tourism in the second half of 2017. Worldwide, international tourist arrivals increased by 6% compared to the same semester of the previous year, far surpassing the sustained and constant growth trend of at least 4% observed since 2010 [31]. These figures give the first six months of 2017 the best semi-annual results obtained in the last seven years. The results are related to the strong growth registered in many destinations and the continuing recovery in those that had registered falls in previous years. Of all WTO regions, growth was greatest in the Middle East (+9%), Europe (+8%) and Africa (+8%), followed by Asia and the Pacific (+6%) and then the Americas (+13%) [27]. In addition, the WTO research concludes that the growth of arrivals was driven by the demand for outbound tourism from the main sourcing markets. In particular, Canada, China, the Republic of Korea, Spain (object of our investigation), the United States, France and the United Kingdom, which have continued to report strong expenditure growth of outbound tourism. 1.3. The Context of Hotel Tourism in Spain When studying the state of tourism in Spain, which is the geographical area on which this study is centered, we found that Spain received 75.6 million tourists in 2016. These figures are 10.3% higher than for 2015. In the last month of 2016, Spain was visited by 4 million foreign tourists, a figure that means an advance of 13.3% on the previous year. In Spain, this sector is a very important part of the national economy, contributing to nearly 11% of GDP (Gross Domestic Product) [27]. Tourists have also increased their spending to 77,000 million Euros. Tourists in Spain usually stay for between 4 and 7 nights. The main tourist destination in Spain was Catalonia. With 18 million tourists, it received 4% more visitors than in 2015. Next were the Canary Islands (with 13.3 million and an increase of 13.2%) and Illes Balears (with 13 million which means an increase of 11.9%) [27,28]. To understand the relevance of the analysis of the best hotels in Spain, we can see the results of FRONTUR’s research in Table 2. This shows that the number of tourists using paid accommodation as their main type of accommodation increased by 15.1% annually in December. In this market, hotel accommodation increased by 11.2% and rented housing increased by 49.2%. Unpaid accommodation showed an increase of 9.1%. Tourists housed in family or friends homes increased by 0.3% and those staying in self-owned housing by 42.5% [28]. Int. J. Environ. Res. Public Health 2018, 15, 553 4 of 20 Table 2. International tourists depending on type of accommodation. Monthly Data Accumulated Data Accumulated Data Type of Accommodation Yearly Variation Absolute Value Absolute Value Yearly Variation TOTAL 3.979.713 13.3 75.563.198 10.3 Total paid accommodation (3) 2.841.464 15.1 59.419.138 11.7 —Hotel accommodation 2.299.988 11.2 47.726.623 11.2 —Rented housing 397.010 49.2 8.278.525 8.0 —Other paid accommodation 144.467 6.4 3.413.990 32.1 Unpaid accommodation 1.138.248 9.1 16.144.060 5.3 —Self-owned housing 320.539 42.5 5.039.040 15.7 —Relatives or friends house 704.622 0.3 9.550.942 9.4 —Other unpaid accommodation 113.087 2.4 1.554.079 31.1 In order to calculate the consequences tourism has on the environment it is also useful to see how these travelers arrived. The largest number of tourists to Spain arrived by air transport (December 2016) with almost 3.2 million arrivals, which represents an annual growth of 16.6%. By road, 0.1% more tourists arrived than in December 2015, while 41.3% more entered by rail and 8.8% more through sea ports, as can be seen in Table 3 [22]. Table 3. Arrival of international tourists according to access routes. Monthly Data Accumulated Data Yearly Variation Yearly Variation Access Routes Absolute Value Absolute Value TOTAL 3.979.713 13.3 75.563.198 10.3 Airport 3.197.756 16.6 60.582.406 11.7 Highway 696.586 0.1 13.038.391 4.4 Train 23.038 41.3 364.115 6.2 Puerto 62.332 8.8 1.578.287 9.6 This data emphasizes the importance that tourism has in the Spanish economy. In order to guarantee its future, companies and especially hotels, must identify the parts of their businesses that affect the environment [21,22]. One of the most important factors to be considered is the protection of the environment. This concept includes many points, not only landscapes and natural resources, but is also closely linked to the quality of the services which are offered and is a variable that constantly appears in the tourism sector [22]. This research can therefore be seen to be of interest to the management of hotels, who have to understand that the activities of their hotels and related services affect the environment and world sustainability. Consequently, it is important for them to improve their services by detecting if the hotel users considered that the environment is well treated or not during their stay at the hotel. Hotels should recognize the importance of this sector as a resource that represents the environment, so that it is not harmed in any way and therefore does not affect the current and future economic and social stability, which is a very important sector in the global economy. The future proposal for the tourism sector is based on quality and sustainable tourism which respects the environment [21]. Tourism and recreational activities depend far more than other sectors, on the quality of the natural and cultural environment for their long-term success. In this way, when a country which has attractive areas for tourism becomes an interesting destination for tourism and recreational activities, uncontrolled environmental impacts can jeopardize future benefits [22]. Therefore, tourism can affect the natural environment to the point of endangering its existence, hence the importance of identifying factors that affect the environment as a result of tourism activities in hotels. In this sense, with the developments produced in sectors such as environmental, cultural, economic and social sustainability, this research is concerned with the identification of environment factors from the analysis of the Twitter users’ opinions for the 25 Hotels that won the Traveler ’s Choice Int. J. Environ. Res. Public Health 2018, 15, 553 5 of 20 award from TripAdvisor in 2017. In this exploratory study, Twitter as a means of commenting on a topic, the opinions of hotel users and the identification of key environment factors that affect hotel visitors will be studied. To complete this exploratory study a Sentiment analysis was carried out using an algorithm developed in Python by MonkeyLearn API (MonkeyLearn, San Francisco, CA, USA) [32] with which 14,459 tweets were grouped according to feeling (positive, negative or neutral) and then a textual analysis was performed on these tweets to identify the main environmental factors from the feelings shown using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). Afterwards, a linear correlation analysis and difference between means test were done using IBM SPSS version 24 (IBM, Chicago, IL, USA) and the analysis and results were presented. Finally, a discussions and conclusions section was written for the results obtained. 2. Related Work 2.1. Machine Learning and Sentiment Analysis Approaches for Social Network Analysis There are several researchers that have developed models based on machine learning for the analysis of social networks, the opinions that users have or to identify key factors related to a specific theme [33,34]. Supervised methods based on classification and categorization of key factors such as MaximumEntropy (MaxEnt) and Support Vector Machines (SVMs) are used for a combination of features to perform social network analysis with machine learning and research methods based on technology to identify the important factors of a research category [7,35–37]. These investigations can be based on keywords, ratings of feelings regarding a topic, semantic meaning, concepts and semantic theories, sentiment-topic features such as hashtags, retweets or points on social networks, and valuation identifiers for products and services on the Internet [38,39]. The research of Pak and Paroubek [40] presents an in-depth development of methodologies based on a study of Twitter. In general, semantic approaches based on sentiment analysis determine the occurrence of the key words, to which a statistical factor has been added. This factor determines the feeling [41] and is often used to perform analysis of positive and negative feelings and shows that Twitter can be an appropriate platform for researching factors of interest [42,43]. In the research by Zhang, Yun, Liang y Zhang [44] a semi-supervised dual recurrent neural network is proposed to prepare a Sentiment Analysis. This is similar to traditional neural networks and can be used to evaluate a set of data over a long period of time. This technique allows a more effective and efficient sentiment analysis to be carried out. In Turney y Pantel [45], a recursive neural network is used to understand the meaning of particular comments. To achieve this, sentiment analysis identifies words and offers the semantic meaning for the particular topic of interest [46–48]. Neural networks are used to establish labels for each word and classify them according to predetermined criteria. The research of Robinson [49] proposed a probabilistic model that is called Textual-based Information Diffusion and Evolution (TIDE), with which the evolution of different topics in social communities and their diffusion over time was measured. This method was based on Sentiment Analysis. The model extracts characteristics from the text of the comments made and implicitly captures them using standard ranges of the Gaussian field, as other authors have done [50–53]. Table 4 shows a summary of the main research using Sentiment Analysis and textual analysis combined with other methodologies based on machine learning and semantic analysis. Int. J. Environ. Res. Public Health 2018, 15, 553 6 of 20 Table 4. Characteristics of Sentiment Analysis in investigations. References Characteristics Pak et al. Kuo et al. Honeycutts Kouloumpis Rodríguez-Herráez Boyd et al. This [34] [9] et al. [50] et al. [54] [41] [51] Research p p p p p Neuronal Connection - - p p p Textual analysis - - - - Time - - - - - - p p p Hashtags, URLs or mentions - - - - p p p Topic - - - - p p p Classification of information - - - - 2.2. Textual Analysis Textual analysis is a qualitative and exploratory procedure that determines the key factors of an event or object of study by grouping them into topic nodes. The Nvivo qualitative analysis software is one of the most relevant in this research category and has been used on numerous occasions in the last decade as a research method [55–58]. In Vázquez and Escamilla [52] a qualitative approach using the Nvivo software is taken to determine the attitudes of older people in order to perform a textual analysis on the contents of the results of the research. Ramirez-Andreotta et al. [53] also carry out a textual analysis to determine and understand the short- and long-term impacts of bio-monitoring and exposure of the participants in the study, in order to identify future factors for environmental justice using the Nvivo software. In the work of Saito, Nakano and Kimura [59] a probabilistic matrix for the prediction of re-tweets based on textual analysis was developed. The social context of the relationships between the messages and their time latency were studied. Likewise, Jiang et al. [60] analyzed the fundamental factors that affect a concept called “re-tweetability” of each tweet when using a predictive filter based on collaboration between users. Textual analysis is used to determine and identify the most repeated factors of the study, and, based on these, determine the corresponding actions for the investigation. In Table 5 the research using textual analysis can be seen, along with the main characteristics of qualitative analysis software such as Nvivo, which includes classification into nodes, categorization by topic, number of times a keyword is repeated and the type of keywords that are repeated. Table 5. Characteristics of textual analysis in investigations. References Characteristics Kwon et Ramirez-Andreotta Rosa et al. Honeycutt Boyd et Saito et Jiang et This al. [17] [18] [48] et al. [50] al. [51] al. [52] al. [53] Research p p p p p p p p Classification into nodes p p p p p Categorization - - - p p p p p p Word Count - - p p p Key word - - - - - 3. Conceptual Framework and Hypothesis Development As we have already outlined, the interest of researchers over the last decades has been focused on the identification and analysis of key determinant factors in social networks with regard to a specific topic. Suanmali [24] detects the environment factors that affect the satisfaction of tourists by using an empirical study in the Northern Part of Thailand and links these factors to the actions carried out by hotels in the geographical area in which they are located. Kaltenborn [25] also investigates the attitudes of tourists to the environmental, social and managerial attributes of Serengeti National Park, concluding with the identification of factors that affect the interest of users and then defines and suggests actions that can be taken by the hotel sector to respect and conserve the environment. The environmental actions carried out by hotels are those actions related to the improvement of services based on social policies, the promotion of traditional products or local consumer goods, services related to the quality of the air or the mountain areas where the facilities are Int. J. Environ. Res. Public Health 2018, 15, 553 7 of 20 located, etc. Pak and Paroubek [40] carried out research in which a recursive neural network was used to understand what a particular content says and categorizes it with respect to the identification of specific terms. Pak and Paroubek [40] undertake a sentiment analysis to identify key words and offer the semantic meaning for each of the positive, negative or neutral links [42]. Based on the statements made above, it is proposed that: Hypothesis 1 (H1). The type of consumer experience in Spanish hotels (positive, negative or neutral) influences the environmental actions carried out by hotels. As indicated previously by Bruyere [26], who identified the feelings of users who visited hotels in rural areas and how these influenced the users’ experiences in hotels in those areas. Eliam [27] also investigates user attitudes to the environment and identifies the key factors that were expressed. Likewise, Moreno et al. [12] develops an enriched consumer recruitment system to increase the retention of users in campaigns for community schemes on social networks, classifying the key factors that was shared in the content and relating them to the research objective [61]. The factors that users consider to be relevant to the environment in hotels can determine their satisfaction with the services contracted in the hotel and can modify their enjoyment in a positive or negative way. The work of Roshan et al. [11] focuses on companies in different periods in order to categorize what the most important factors are when assessing the users’ experience with the companies. Roshan et al. [11] evaluate users experiences and opinions, and determine key factors from these [62]. Therefore, the following hypothesis is presented: Hypothesis 2 (H2). The environmental factors that users of Spanish hotels observe during their stays influence their experience at the hotel. 4. Methodology The methodology used was firstly to perform a Sentiment Analysis on Twitter posts [7,33]. Using the results of this Sentiment analysis, a textual analysis was done to identify the key factors related to the environment [44,46]. Finally, bivariate linear correlation was used to demonstrate the statistical significance of the results [63] and then a difference of means test was done on the type of hotels, which were grouped according to their star ratings. This test was based on the Levene Test using the statistical analysis program IBM SPSS version 24. 4.1. Sample As we have already indicated, the objective of this exploratory study is to identify the positive, neutral and negative factors related to the environment that affect the experiences of hotel users. These factors can help hotel management to improve their services and propose new social responsibility strategies for sustainability and respect for the environment such as, tourist activities which respect the environment or the promotion of activities that do not pollute the environment. The research sample is composed of Spanish hotels ranked using TripAdvisor Traveler ’s Choice Awards, which draws from more than 500 million opinions from travelers in Spain. Also, it is important to notice that Spain has become the second most visited country in world (82 million tourists visited Spain in 2018 according to data from the government of Spain). To identify the factors related to the environment, we analyzed the reviews that hotel users made on Twitter between 1 October 2016 and 1 October 2017 for the 25 hotels that won the Traveller ’s Choice Awards. A total of n = 14,459 tweets for the 25 hotels that make up the sample were analyzed [64]. As we have already indicated, the research sample is made up of the Twitter profiles of the 25 hotels that won the TripAdvisor Traveller ’s Choice Awards in 2017 and that have been classified according to: Active and official profile on Twitter Int. J. Environ. Res. Public Health 2018, 15, 553 8 of 20 Public profile Number of opinions in tweets Number of interactions with users Appendix A shows the identification of the hotels under study in TripAdvisor and Twitter. 4.2. Data Collection and Extraction Data was collected using the Twitter API between 1 October 2016 and 1 October 2017. To carry out the Sentiment Analysis, we used the access algorithm in the MonkeyLearn API [32] that is written in Python and uses machine learning techniques to improve the levels of prediction and significance. Firstly, the data was collected and classified using the Twitter API. Then, after having thoroughly trained the algorithm that does the Sentiment Analysis with part of the sample data, the entire database was analysed. The tweets for each hotel were divided into negative, positive and neutral groupings. Thirdly, a textual analysis was performed using the Nvivo 11 software (QSR International, Melbourne, Australia). The tweets were separated into nodes according to the feelings expressed (N , N and N ) 1 2 3 and then thematic nodes were identified to test N , which is made up of the key environment factors. 4.3. Textual Analysis The next step was to perform a textual analysis to identify the environment factors in the analyzed tweets. We used the Nvivo software to do this as it allows us to completely configure the Analysis for our test purpose [22]. The Nvivo software allows for classification by Nodes. The Nodes are configured as containers for the information which includes the evidence and has already been grouped beforehand [65–67]. It must be emphasized that the creation, design and exploration of nodes is a way to research pure data, in order to achieve higher quality descriptive and explanatory levels than could be reached without it [68]. Free nodes are data containers that can group ideas separately and that are not conceptually related to other nodes in an analysis. Branched nodes, are used to represent concepts grouped by categories which are logically linked and can be grouped hierarchically. The analysis results are then presented and are characterized using different indicators [69]. To do this we have established different nodes to identify the main environmental factors from the feelings examined with the nodes [70]. Firstly, a textual analysis was carried out on environmental factors in the tweets, classified as negative feelings (N ), secondly, with the tweets classified as neutral feelings (N ) and thirdly with the tweets classified as positive feelings (N ). 2 3 An exploratory analysis of each of the nodes allowed us to identify environment factors which were grouped into the node (N ) and then divided into three categories. Table 6 shows the classification of the nodes according to negative, neutral and positive environment factors [71]. Table 6. Environmental Node classification with Nvivo software. Factor Identification Nodes Negative Node 1 (N ) Neutral Node 2 (N ) Positive Node 3 (N ) 5. Findings 5.1. Sentiment Analysis In order to identify the environment factors observed by hotel users, the results of the textual analysis were analyzed for each of the nodes [72]. Firstly, the classifications which were used for the Sentiment Analysis carried out with machine learning are presented. These classify all of the users’ opinion tweets into positive, negative and neutral [73]. Int. J. Environ. Res. Public Health 2018, 15, 553 9 of 20 Next, in Table 7, the probability coefficients obtained from the classification of the Sentiment Analysis are presented with a summary of the content averages for each feeling. Then, the results of Int. J. Environ. Res. Public Health 2018, 15, x 9 of 20 the textual analysis are presented for the nodes that subdivide each of the topics and factors that have Next, in Table 7, the probability coefficients obtained from the classification of the Sentiment been identified in the research. Analysis are presented with a summary of the content averages for each feeling. Then, the results of the textual analysis are presented for the nodes that subdivide each of the topics and factors that have Table 7. Environment data which was analyzed and average classification probability percentages for been identified in the research. each Hotel. Table 7. Environment data which was analyzed and average classification probability percentages for Traveller’s Choice from TripAdvisor 2017 Tweets Negative Neutral Positive Average Probability each Hotel. Hotel The Serras 159 1 90 68 0.639 Traveller’s Choice from TripAdvisor 2017 Tweets Negative Neutral Positive Average Probability Vincci Selección Aleysa Hotel Boutique & Spa 1665 81 734 850 0.610 Hotel The Serras 159 1 90 68 0.639 Casa Camper Hotel Barcelona 52 1 39 12 0.555 Vincci Selección Aleysa Hotel Boutique & Spa 1665 81 734 850 0.610 Hotel Orfila 436 1 419 16 0.662 Casa Camper Hotel Barcelona 52 1 39 12 0.555 Hotel Abadia Retuerta Le Domaine 399 15 180 204 0.620 Gran Hotel Son Net 213 - 161 52 0.616 Hotel Orfila 436 1 419 16 0.662 Hotel Maria Cristina 211 10 100 101 0.614 Hotel Abadia Retuerta Le Domaine 399 15 180 204 0.620 Only YOU Boutique Hotel Madrid 756 12 383 361 0.651 Gran Hotel Son Net 213 - 161 52 0.616 Alma Pamplona Muga de Beloso 40 1 16 23 0.674 Hotel Maria Cristina 211 10 100 101 0.614 Seaside Grand Hotel Residencia 128 58 938 726 0.624 Only YOU Boutique Hotel Madrid 756 12 383 361 0.651 Hotel Hacienda de Abajo 80 - 44 37 0.666 Alma Pamplona Muga de Beloso 40 1 16 23 0.674 Hotel Olivia Balmés 35 - 28 8 0.609 Seaside Grand Hotel Residencia 128 58 938 726 0.624 Riviera Beachotel 369 9 225 136 0.682 Hotel Hacienda de Abajo 80 - 44 37 0.666 Sant Francesc Hotel Singular 355 - 296 60 0.598 Hotel Olivia Balmés 35 - 28 8 0.609 El Palace Hotel 373 19 173 182 0.600 Riviera Beachotel 369 9 225 136 0.682 Gran Hotel La Perla 1539 86 670 784 0.607 Sant Francesc Hotel Singular 355 - 296 60 0.598 Barceló Emperatriz 1722 59 938 726 0.624 El Palace Hotel 373 19 173 182 0.600 Hotel Astoria Playa Only Adults 93 1 62 30 0.596 Gran Hotel La Perla 1539 86 670 784 0.607 Catalonia Square 2762 32 1859 935 0.652 Barceló Emperatriz 1722 59 938 726 0.624 H10 Cubik 1407 8 1214 185 0.711 Hotel Astoria Playa Only Adults 93 1 62 30 0.596 Gold By Marina 720 19 398 304 0.622 Catalonia Square 2762 32 1859 935 0.652 Hotel Spa Relais 945 34 437 475 0.679 H10 Cubik 1407 8 1214 185 0.711 n = 14,459 Gold By Marina 720 19 398 304 0.622 Hotel Spa Relais 945 34 437 475 0.679 n = 14,459 The total number of analyzed tweets, n was 14,459. The average of published tweets was 657.22 The total number of analyzed tweets, n was 14,459. The average of published tweets was 657.22 and from the machine learning Sentiment Analysis the greatest probability percentage was 0.679 and and from the machine learning Sentiment Analysis the greatest probability percentage was 0.679 and the least was 0.555 [74]. the least was 0.555 [74]. The probability percentages resulting from the Sentiment Analysis for each tweet classification The probability percentages resulting from the Sentiment Analysis for each tweet classification can be seen in Figure 1. The probability percentage is a measure of accuracy, precision and recall of the can be seen in Figure 1. The probability percentage is a measure of accuracy, precision and recall of samples in each category. the samples in each category. Figure 1. Classification of tweets according to feelings about the environment. Figure 1. Classification of tweets according to feelings about the environment. From all the analyzed tweets, 477 were negative, 7737 were neutral and 6275 were positive, which show that Twitter can be used as a platform to establish a relationship with the user [75]. From all the analyzed tweets, 477 were negative, 7737 were neutral and 6275 were positive, which show that Twitter can be used as a platform to establish a relationship with the user [75]. 5.2. Textual Analysis 5.2. Textual Analysis To identify the key environment factors in these user interactions, we carried out a textual analysis with Nvivo, for which each of the nodes of the textual analysis was divided into positive, To identify the key environment factors in these user interactions, we carried out a textual analysis negative and neutral nodes for the environment factors [76]. with Nvivo, for which each of the nodes of the textual analysis was divided into positive, negative and In Figure 2 below, we can the results of the global textual analysis after the Sentiment Analysis, and the environment factors which were tested [77]. neutral nodes for the environment factors [76]. In Figure 2 below, we can the results of the global textual analysis after the Sentiment Analysis, and the environment factors which were tested [77]. Int. J. Environ. Res. Public Health 2018, 15, 553 10 of 20 Int. J. Environ. Res. Public Health 2018, 15, x 10 of 20 The textual analysis of the environmental factors in N1 found the following results when The textual analysis of the environmental factors in N found the following results when analyzing analyzing the global opinions of users about their experience in the hotel [78]. Table 8 shows the the global opinions of users about their experience in the hotel [78]. Table 8 shows the Semantic analysis Semantic analysis for negative environmental factors and Table 9 shows three negative tweets for negative environmental factors and Table 9 shows three negative tweets examples. examples. Figure 2. Relationship of Nodes and number of tweets for environment factors identification. Figure 2. Relationship of Nodes and number of tweets for environment factors identification. Table 8. Results for N1 for environment factors identification. Table 8. Results for N for environment factors identification. N1 Count Similar Factors Weighted Percentage Rubbish collection 16 rubbish, trash, waste 0.06 Atmospheric contamination 15 contamination, breathing, asthma 0.06 N Count Similar Factors Weighted Percentage Pollution 14 dirty, dirt 0.06 Rubbish collection 16 rubbish, trash, waste 0.06 Atmospheric contamination 15 contamination, breathing, asthma 0.06 Table 9. Results for N2 for environment factors identification. Pollution 14 dirty, dirt 0.06 Negative Tweets 1. Really disgusted by the unhygienic food that @barcelohoteles are serving at the #AllegroIsora really regret Table 9. Results for N for environment factors identification. booking here for 8 days when staff won’t do anything about it. 2. @H10_Hotels we are at your resort in Punta Cana sick with food poisoning management does not care. Negative Tweets Many guests are sick We will never return 3. @El_Felips paid for a upgraded room in Barcelo Lanzarote, so disappointed the room is tired, air con very 1. Really disgusted by the unhygienic food that @barcelohoteles are serving at the #AllegroIsora really regret poor, mould in bathroom and smelling very bad!! The installations and trips to locals places are also bad! booking here for 8 days when staff won’t do anything about it. 2. @H10_Hotels we are at your resort in Punta Cana sick with food poisoning management does not care. The negative factors in N1 show the users’ concerns about garbage collection, air pollution and Many guests are sick We will never return the pollution of the hotels’ environment [76] 3. @El_Felips paid for a upgraded room in Barcelo Lanzarote, so disappointed the room is tired, air con very These results demonstrate the users concerns about negative environment factors. The weighted poor, mould in bathroom and smelling very bad!! The installations and trips to locals places are also bad! percentages do not exceed 0.06 for the negative content, so we can affirm that the hotel users are not highly concerned about these environmental factors [76]. The environment factors for N2, neutral factors, are shown in Table 10 below and Table 11 shows The negative factors in N show the users’ concerns about garbage collection, air pollution and three neutral tweets examples. the pollution of the hotels’ environment [76] Table 10. Results for N2 for environment factors identification. These results demonstrate the users concerns about negative environment factors. The weighted N2 Count Similar Factors Weighted Percentage percentages do not exceed 0.06 for the negative content, so we can affirm that the hotel users are not Local products 250 Streetmarket, markets, local produce 0.40 highly concerned about these environmental factors [76]. Handcraft 71 artisan, craftsmen, pottery 0.11 The environment factors for N , neutral factors, are shown in Table 10 below and Table 11 shows Traditional experience 267 shelters, seminars, monasteries 0.11 Looking after nature 57 rivers, villages, mountains, roads 0.09 three neutral tweets examples. Table 11. Neutral tweets examples. Table 10. Results for N for environment factors identification. Negative Tweets 1. RT @AnneSemonin We love to visit Hotel Sant Francesc for a spot of sunshine and a luxury Anne Semonin N Count Similar Factors Weighted Percentage treatment. Local products 250 Streetmarket, markets, local produce 0.40 2. RT @Stanatic_Ness: A beautiful street with so much of history and tradition #PalmaDeMallorca #Spain Handcraft 71 artisan, craftsmen, pottery 0.11 3. Eating a local fish in #LaPalma @haciendadeabajo-medregal-tastes a bit like tuna, v tasty Traditional experience 67 shelters, seminars, monasteries 0.11 Looking after nature 57 rivers, villages, mountains, roads 0.09 Table 11. Neutral tweets examples. Negative Tweets 1. RT @AnneSemonin We love to visit Hotel Sant Francesc for a spot of sunshine and a luxury Anne Semonin treatment. 2. RT @Stanatic_Ness: A beautiful street with so much of history and tradition #PalmaDeMallorca #Spain 3. Eating a local fish in #LaPalma @haciendadeabajo-medregal-tastes a bit like tuna, v tasty Int. J. Environ. Res. Public Health 2018, 15, 553 11 of 20 The textual analysis of quality of service factors (N ) gave results showing how the users consider the products and services that the hotel makes available for planned trips and specialized offers. Neutral environment factors were found, such as the importance of local products in local markets and shops, crafts from local potteries and artisans, traditional experiences such as visits to shelters, seminars and local monasteries that have not suffered changes over the years and finally a respect for nature, rivers and geographical features when constructing villages, along with the preservation of mountains and local roads. By using weighted percentages for N , it can be seen that users give greater importance to N . The highest weighted percentage for environment factors in the textual analysis was 0.40. Finally, the results of the textual analysis for environment factors of the 6275 tweets that make up the N node can be seen in Table 12. Also, Table 13 shows three neutral tweets examples. Table 12. Results for N for environment factors identification. N Count Similar Factors Weighted Percentage Local Traditions 67 hostels, routes, local products, shows 0.29 Air Quality 32 clarity, pure, sight, breathing, asthma 0.12 Customs 44 dances, traditional production, pastry making, viticulture 0.21 Traditional Ecology 12 saltpans, orchards, eco-products 0.10 Nature 11 oasis, islands, mountain, rivers, trails 0.09 Table 13. Positive tweets examples. Positive Tweets 1. #AQuintadaAuga is a #hotel surrounded by #nature in #SantiagodeCompostela. It is a mandatory stop 2. RT @AmyWorsley85: Always looking for a #sustainable place to stay, like the @Olivia_Balmes hotel in #Barcelona which was great! 3. @barcelohoteles @Bobadilla5GL Congrats! More hotels can be sustainable using local biomass for energy. Please visit us at @conectabioener! A great variety of similar environment factors were found for N . In particular, different contents about local tradition, customs, air quality, traditional ecology and the importance of nature were analyzed and evaluated. The users valued positively the environment factors regarding hostels, routes, local products and shows. In addition, use showed positive relationships with the environments of dances and traditional customs, such as pastry making and viticulture. Another very important factor is the quality of the air, which can be linked to breathing problems, asthma and other diseases [73]. Traditional ecology is represented by factors such as salt pans, orchards and organic products. In general, nature is linked to concepts such as oasis, islands, mountains, rivers and paths. The weighted percentages of the textual analysis of N are between 0.39 and 0.09 in terms of positive factors related to the environment. Table 14 shows all the environmental factors identified as a result of the Sentiment Analysis and subsequent textual analysis of the opinions and tweets of the hotel users that make up the sample. Table 11 summarizes the main environmental factors in the users’ opinions for the hotels of the 2017 Traveler ’s Award and can be used to highlight the most important factors for the consumer. That is to say, the analysis of the results shows how the Spanish hotels users express their attitudes towards the environment factors which influence their experiences. The results of this exploratory study can be used by hotel management to pay attention to the factors which are shown to be important to the guests. The positive factors that affect users visiting Spanish hotels are directly related to the activities carried out or managed by the hotels and which are associated with local traditions, air quality, customs, traditional ecology or nature. Hotel management can therefore organize activities that promote these factors to improve the users’ experience. Int. J. Environ. Res. Public Health 2018, 15, 553 12 of 20 Table 14. Results of N for environment factors identification. Nodes Environment Factors Related Factors Total Count Local Traditions hostels, routes, local products, shows clarity, Air Quality pure, sight, breathing, asthma, dances, Positive Factors Customs traditional production, pastry making, 166 Traditional Ecology viticulture, saltpans, orchards, eco-products, Nature oasis, islands, mountain, rivers, trails Local products street market, markets, local produce Handcraft artisan, craftsmen, pottery Neutral Factors 445 Traditional experience shelters, seminars, monasteries Looking after nature rivers, villages, mountains, roads Garbage collection rubbish, trash, waste Negative Factors Atmospheric pollution contamination, breathing, asthma 45 Pollution dirty, dirt The results also show that the activities provided by the hotels with local and artisan products, traditional experiences and care for nature are factors that are neutral for visitors. However, the results of the exploratory study show that hotel management can use these results to improve their activities, initiatives and services, taking into account that the users regard garbage collection, atmospheric pollution and pollution as negative environment factors. 5.3. Linear Correlation Analysis and Difference between Means Tests In order to show the statistical significances as an additional analysis of the investigation results, a bivariate linear correlation was made to check the possible relationships between the variables. The dependent variables were the number of followers on Twitter, and the negative, neutral or positive feeling of the tweets that were in the results [63]. The independent variables are the number of stars that the hotels under study have on TripAdvisor, the number of comments in the official Twitter profiles and the number of followers [64,65]. This test is used to check the possible relationship between two metric variables. The measurement is carried out using the linear correlation coefficient, which ranges between 1 and 1. If the value is 1 and positive, it indicates variation in the same direction for both variables. A negative value of 1 indicates variation in opposite directions, which means that when one of the variables increases, the other decreases. A value close to zero means that the variables behave independently. Therefore, correlation shows the extent to which two variables share a variation. To calculate the percentage of joint variation, the coefficient of determination is used, which is the square of the linear correlation coefficient. Correlations measure how variables or rank orders are related. The results of the correlations are shown in Table 15. Table 15. Bivariate linear correlation results for environmental factors. Number of Environment Number of Number of Correlation Results Stars Comments Environment Tweets Followers Pearson Correlation 0.220 0.183 0.588 ** 1 Number of Sig. (bilateral) 0.326 0.415 0.006 Followers N 22 22 20 22 Pearson Correlation 0.183 0.325 0.648 ** 0.371 Negative Sig. (bilateral) 0.414 0.140 0.002 0.089 Tweets N 22 22 20 22 Pearson Correlation 0.152 0.339 0.780 ** 0.339 Sig. (bilateral) 0.499 0.123 0.000 0.122 Neutral Tweets N 22 22 20 22 Pearson Correlation 0.068 0.303 0.688 ** 0.381 Positive Tweets Sig. (bilateral) 0.764 0.171 0.001 0.081 N 22 22 20 22 ** Significance intensity. Int. J. Environ. Res. Public Health 2018, 15, 553 13 of 20 High correlation between five variables can be seen: Number of Environment Tweets, Number of Followers, Negative Environment Tweets, Neutral Environment Tweets and Positive Environment Tweets, and also high significance (**) of two-by-two correlation coefficients. This can be between 1 (a perfect negative relationship) and +1 (a perfect positive relationship). A value of 0 indicates that there is no linear relationship. The test for significance was unilateral probability, since the direction of association was Number of Environment Tweets towards the type of Tweets and Number of Followers towards Number of Tweets. Correlations with significant correlation coefficients (p  0.05) are indicated with a single asterisk and those with (p  0.01) are identified by two asterisks. The results show that there are significant correlations (p  0.01) between Number of Tweets and Negative Environment Tweets, Neutral Environment Tweet and Positive Environment Tweets. In all three cases correlation is positive and close to +1. Also, there is significant correlation (p  0.01) between Number of Followers and Number of Environment Tweets. In addition, we can conclude that the higher the number of tweets in a hotel profile, the higher the number of negative tweets (Pearson correlation = 0.648, p  0.01). However, the relationship is even more significant if we take into account that the higher the number of tweets made for the hotel, the higher the number of neutral comments (Pearson correlation = 0.780, p  0.001) and negative comments (Pearson correlation = 0.688; p  0.001). The coefficient of determination (R ) shows that 47% of the changes in the number of Tweets correspond to change in the number of negative tweets. The highest value of R , 60.8%, is obtained by the number of neutral environmental tweets. In addition, a descriptive statistical analysis was carried out using a difference of means test. It is quite frequent in market research to hypothesize whether the differences in behavior detected in two population subsamples give sufficient evidence for differences in the populations or if, on the contrary, they are only a product of sampling error. Before applying the mean difference test or Student’s t test, any statistically significant differences when comparing the averages of the two independent subsamples must be determined. The Levene Test must be performed to check whether the variances are different or not. A predictive model is said to present homoscedasticity when the variance of the error of the endogenous variable is maintained throughout the observations. Therefore, this condition is checked before carrying out the hypothesis test that leads to the t test. To do this, we divided the sample into two groups: 5-star hotels and 4.5-star hotels. As can be seen in Table 16, the difference in means was not significant, except in negative comments (p  0.05), where there are differences and where there are more negative comments in hotels with fewer stars. Table 16. Differences of means test results. Difference of Difference of Variable F Sig. t Means Standard Error Nº comments 1.363 0.257 1.687 510.933 302.795 Tweets 0.066 0.800 0.414 725.000 1750.570 Followers 2.943 0.102 1.006 16,259.150 16,157.287 Negative 7.454 0.013 0.833 9.683 11.620 Neutral 1.483 0.237 0.689 139.617 202.547 Positive 0.932 0.346 0.305 41.933 137.628 F: F Square; Sig.: Significance level; t: t-Student. 6. Discussion As the work of Honeycutt and Herring [50] indicates, the opinions and reviews of users can be analyzed from a consumer satisfaction perspective since concerns and opinions are identified regarding Int. J. Environ. Res. Public Health 2018, 15, 553 14 of 20 the purchase of a product, or use of a service [31,41]. Furthermore Agarwal et al. [35] indicates that an exploratory study can be carried out to link these concepts with any particular research theme [37]. In this way, this exploratory study links the main environment factors for the users who have visited hotels that appear in the Traveler ’s Ranking 2017. The work of Kwon [17] shows Sentiment Analysis as a research process that allows for correct classification of users’ opinions and reviews on social networks and 2.0 platforms. Social networks have given companies new ways to receive the impressions and expectations of their customers. The works of Rosa, Batista and Carvalho [42] and Halog [52] show the need to understand these new phenomena and above all to know how to identify problems and key factors. To do this in our exploratory study, key factors were identified that could help companies in the tourism sector to improve the quality of their services and offer better products [20] based on the improvement of environment factors identified in the investigation [7,13]. As stated, after conducting the Sentiment Analysis, a total of n = 14,459 tweets were identified for the sample of hotel users. These tweets were classified as negative, neutral and positive with an algorithm developed in Python and based on machine learning by MonkeyLearn API [7,26]. As we have already verified from the analysis of the research results by using a qualitative approach on the analyzed environment indicators, Twitter has been shown to be a valid platform for research on the environment. This exploratory study shows that although some Twitter users make complaints or take advantage of offers and discounts, many users comment on the environmental factors which determined their expectations and experiences at hotels. Therefore, this exploratory study demonstrates that it is possible to determine the relationships that hotel users in Spain have with the key environment factors that can be improved or attended to by hotels such as those related to activities carried out by the hotel and the impact of these on the environment, on air quality or on the pollution generated by these activities. This can be seen because the hotel users show interest in these factors in their tweets and so they consequently partially determine the satisfaction and experience that the visitors have when they stay at the hotels. In addition, the quantitative analysis finds that a greater number of followers on Twitter does not mean that these followers are positive in their comments. In fact, the exploratory study shows that negative comments increase when hotels have more followers. This is due to the users who experience negative environmental factors usually follow the hotel’s Twitter profile to make complaints or indicate dissatisfaction with the services provided. Also, as a result of the quantitative analysis we saw that the number of followers on Twitter does not necessarily increase the number of positive comments of the hotel users in the Spanish hotel sector. We also found that if the number of followers is greater, the neutral comments also increase which means that hotel users use Twitter as a source of information for activities, updates and monitoring of hotel communications, but not necessarily to express satisfaction with the hotel. Twitter has therefore consolidated itself as a social network that can be the object of exploratory study related to the environment. It can also help hotel managers to improve their services by identifying the environment factors which travelers have shown interest in during their stay. 7. Conclusions Over the last few decades, the use of new technologies has led to changes in sectors related to environment, culture, sustainability and global warming [1,5]. The development of new technologies and especially social networks has allowed the tourism sector to incorporate important changes when assessing actions related to the environment and social responsibility policies [6,7]. For this investigation, the hotels which won the Traveler ’s Choice Award, which is based on over 500 million travelers’ reviews on TripAdvisor, were used. These hotels have achieved ratings of between 4.5 and 5.0, with 5 being the highest valuation from people who used their services. The findings of this exploratory study were analyzed to find the environment factors which could concern hotel users, with the negative, neutral and positive environment factors being classified as N , 1 Int. J. Environ. Res. Public Health 2018, 15, 553 15 of 20 N and N . The affect that these environment factors have on the users who visit the hotels was also 2 3 studied (N ) [7]. H1 is confirmed because after the textual analysis, it was seen that factors related to the environment influence the experience that users have in hotels, and these users refer to different factors based on their feelings with opinions and reviews of the hotels. H2 is accepted, as shown by the results of the exploratory study, because the N , N and N structures have been developed and 1 2 3 the main factors that affect hotel users have been structured and classified into three types of feelings; negative, neutral and positive. Likewise, H2 has been accepted since the results of the exploratory study show that there are connections between the reviews that users leave about their experiences in hotels on Twitter with the identification of factors related to the environment. The contribution of this exploratory study is the identification of the key environmental concepts that can help the hotel industry to make improvements in the activities and actions related to the environment, significantly improving the perception that users have of topics related to ecology, traditional commerce, ecological products or air quality, among other factors. Hotels can use the results of this exploratory study to highlight the importance that the factors identified by users have for the hotel sector and act upon them. Therefore, the results of this exploratory study are relevant for hotel management because they can be used to add value to the strategies for improvement of tourist activities so that they respect the environment. Some examples such as hotel activities that could affect the garbage collection or atmospheric pollution in its surroundings. Also, improvements can be made in the hotel activities to support and respect local traditions, improve air quality, support traditional customs and ecology and respect and maintain activities that favor the natural environment of the geographical area in which the hotel is located. This exploratory study shows positive and negative concerns about the quality of services when users travel and use the services and activities organized by the hotels. This shows that travelers really do care about the environment and the consequences of their trips. Spanish hotels can also use the results of this exploratory study to develop new strategies for corporate social responsibility and to correctly identify which environmental factors concern users so that hotel activities can be adapted to incorporate actions that support or disseminate local traditions and products, maintain the local environment and wildlife of rural areas, or respecting air quality in the areas where hotels have facilities and offer services. With the results of this exploratory study Spanish hotels can increase user satisfaction by taking into account the environmental factors that were identified in this study. The limitations of this study are related to the number of subjects that make up the sample, the time frame in which the reviews were made and the qualitative analysis carried out to determine the topics of interest, as well as the Occurrence and Reliability percentages of the results. Further studies could be made into the environment factors detected by hotel users and moderating variables such as age or sex, as well as the geographical areas in which the hotels are located. Also, how the users’ feelings about these factors influence their satisfaction with the hotel and its services, along with the real impact they have on the environment. Author Contributions: Jose Ramon Saura, Pedro Palos-Sanchez and Miguel Angel Rios Martin conceived and designed the review; Jose Ramon Saura performed the methodology; Pedro Palos-Sanchez and Miguel Angel Rios Martin analyzed the results; Jose Ramon Saura, Pedro Palos-Sanchez and Miguel Angel Rios Martin wrote the paper. Conflicts of Interest: The authors declare no conflict of interest. Int. J. Environ. Res. Public Health 2018, 15, 553 16 of 20 Appendix Table A1. Winning traveler ’s choice hotels from TripAdvisor sample in 2017. Traveller’s Choice fromTripAdvisor 2017 Location TripAdvisor Starts Customer Reviews Twitter Profile Tweets Followers Hotel The Serras Barcelona, Spain 5.0 843 @TheSerrasHotel 682 727 VincciSelecciónAleysa Hotel Benalmádena, Spain 5.0 842 @Vincci_Hoteles 11.9 k 19.6 K Casa Camper Hotel Barcelona Barcelona, Spain 5.0 1924 @casacamper 1042 175 Hotel Orfila Madrid, Spain 5.0 111 @HotelOrfila 726 356 Hotel AbadiaRetuerta Le Domaine Sardón de Duero, Spain 5.0 449 @arledomaine 2642 1.365 Gran Hotel Son Net Puigpunyent, Spain 4.5 598 @GranHotelSonNet 1260 3286 Hotel Maria Cristina San Sebastián—Donostia, Spain 4.5 1794 @hotelmariacrist 1592 2218 Only YOU Boutique Hotel Madrid Madrid, Spain 5.0 1975 @OnlyYOUHotels 4512 3877 Alma Pamplona Muga de Beloso Pamplona, Spain 5.0 1187 @almahotels 68 86 La Bobadilla, a Royal Hideaway Hotel Loja, Spain 5.0 774 @barcelohoteles 11.2 k 179 K Seaside Grand Hotel Residencia Maspalomas, Spain 5.0 653 @GrandHotelPunta 753 858 Hotel Hacienda de Abajo Tazacorte, Spain 5.0 259 @HaciendadeAbajo 585 712 Hotel Olivia Balmés Barcelona, Spain 4.5 2304 @Olivia_Balmes 548 7241 Riviera Beachotel Benidorm, Spain 4.5 2314 @benidormhoteles 2912 1859 Sant Francesc Hotel Singular Palma de Mallorca, Spain 4.5 476 @hotelstfrancesc 1490 1409 El Palace Hotel Barcelona, Spain 4.5 1776 @ElPalaceHotel 2152 2512 Gran Hotel La Perla Pamplona, Spain 5.0 336 @Ghotellaperla 6440 3020 Barceló Emperatriz Madrid, Spain 4.5 568 @barcelohoteles 11.2 k 179 K Hotel Astoria Playa OnlyAdults Port d’Alcudia, Spain 4.5 2427 @astoriaplayasup 254 139 Catalonia Square Barcelona, Spain 4.5 932 @CataloniaHotels 22.9 k 13.6 K H10 Cubik Barcelona, Spain 4.5 822 @h10hotels 10.9 k 18.4 K Gold By Marina Playa del Inglés, Spain 4.5 1596 @GoldByMarina 1.594 381 Hotel Spa Relais & ChateauxAuga Santiago de Compostela, Spain 4.5 819 @aquintadaauga 4292 1996 Alma Barcelona Barcelona, Spain 4.5 1876 @almahotels 68 86 Hotel Las Madrigueras Golf Resort and Spa Playa de las Américas, Spain 5.0 305 - - - Int. 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Pollut. 2009, 39, 4–27. [CrossRef] 76. Kim, J.; Xu, M.; Kahhat, R.; Allenby, B.; Williams, E. Designing and assessing a sustainable networked delivery (SND) system: Hybrid business-to-consumer book delivery case study. Environ. Sci. Technol. 2009, 43, 181–187. [CrossRef] [PubMed] 77. Chaffey, D.; Patron, M. From web analytics to digital marketing optimization: Increasing the commercial value of digital analytics. J. Direct Data Dig. Mark. Pract. 2012, 14, 30–45. 78. QSR International Pty Ltd. NVIVO: Reference Guide; QSR International Pty Ltd.: Doncaster, Australia, 2000. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Environmental Research and Public Health Pubmed Central

Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An Exploratory Study

International Journal of Environmental Research and Public Health , Volume 15 (3) – Mar 19, 2018

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Abstract

International Journal of Environmental Research and Public Health Article Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An Exploratory Study 1 , ID 2 ID 3 ID Jose Ramon Saura * , Pedro Palos-Sanchez and Miguel Angel Rios Martin Department of Business and Economics, Faculty of Social Sciences and Law, Rey Juan Carlos University, Paseo Artilleros s/n, Madrid 28032, Spain Department of Business Organization, Marketing and Market Research, International University of La Rioja, Av. de la Paz 137, 26006 Logroño, Spain; ppalos@unex.es Department of Financial Economy and Operations Management, Faculty of Economics and Business, University of de Sevilla, Av. de Ramon y Cajal, 1, Sevilla 41004, Spain; rios@us.es * Correspondence: joseramon.saura@urjc.es; Tel: +91-488-80-11 Received: 12 January 2018; Accepted: 15 March 2018; Published: 19 March 2018 Abstract: The object of this exploratory study is to identify the positive, neutral and negative environment factors that affect users who visit Spanish hotels in order to help the hotel managers decide how to improve the quality of the services provided. To carry out the research a Sentiment Analysis was initially performed, grouping the sample of tweets (n = 14459) according to the feelings shown and then a textual analysis was used to identify the key environment factors in these feelings using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). The results of the exploratory study present the key environment factors that affect the users experience when visiting hotels in Spain, such as actions that support local traditions and products, the maintenance of rural areas respecting the local environment and nature, or respecting air quality in the areas where hotels have facilities and offer services. The conclusions of the research can help hotels improve their services and the impact on the environment, as well as improving the visitors experience based on the positive, neutral and negative environment factors which the visitors themselves identified. Keywords: environment; sentiment analysis; Nvivo; twitter; textual analysis 1. Introduction 1.1. Global Overview of Tourism Trends Over the past few years, the use of new technologies has led to changes in the environmental, cultural, economic and social sustainability sectors from how they were at the beginning of the 21st century [1,2]. Tourism and the activities linked to its growth have become a key part of the world economy and have also made local populations suffer the consequences of the impact of these activities on the environment [3]. Activities for tourism provide important amounts of income and jobs, promote knowledge of other cultures and conserve cultural and natural heritage, as well as investing in local infrastructure [4]. These activities provide both economic and social benefits. However, not everything is positive as some styles of tourism and certain recreational activities can lead to the destruction of habitats, the deterioration of the landscape and competition for scarce resources and services (fresh water, territory, energy, wastewater treatment, etc.) [5]. In addition, local populations may lose their traditional way of life as a consequence of these activities and could also become excessively dependent on the income generated by tourism [6]. Int. J. Environ. Res. Public Health 2018, 15, 553; doi:10.3390/ijerph15030553 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2018, 15, 553 2 of 20 The increase in prices associated with tourism can also negatively affect the local population, which runs the risk of losing land, houses, shops and services for the construction of hotels, hostels, rural houses or tourist services [7]. These problems are worsened by the concentration of tourist activity in relatively short holiday periods and in certain limited areas. These areas may also be subject to environmental pressures from other economic activities such as agriculture, fishing, and industrial development or from the increasing resident population. More than other sectors, tourism and recreational activities depend on the quality of the natural and cultural environment for their long-term success. It can be seen therefore, that tourism can affect the natural environment to the point of endangering its own existence. To stop this, it is important to identify the environment-related factors which hotel users detect during their stays at hotels. These users are aware of the problems and the impact of tourism on the environment [8,9]. It is important to draw attention to the fact that the development of new technologies, and especially social networks has caused important changes to the tourism sector [6]. One great change is that people who stay at hotels can easily make comments on the Internet and provide information about their experience and the quality of their visit [9,10]. Like Hung, Kuo and Lin [6] the research of Bifet and Frank [7] analyzed the posts made on social network by companies in one sector. The results of the research show that the tone and narrative used in the post are directly associated to the interest shown in the post by both mobile device and desktop social networks users, which allows for the identification of the key factors which should be used to establish company strategies [11–14]. For some time, social networks and understanding their use has been of interest to researchers in different sectors, such as environmental, cultural, economic and social sustainability. In the work of Kwon [15–17], in which a qualitative approach is taken to determine the attitudes of older people, or the work of Honeycutt et al. [18] in which a textual analysis is performed to determine and understand the short- and long-term impacts in the field of environmental science and public health. 1.2. Literature Review The interest that researchers have in identifying environmental factors which affect the users’ experiences when staying at hotels must also be considered. The interest of researchers in this area is shown in various research papers, such as those of Hussain and Singh [19], in which the research aims to discover the attitudes and behaviors of hotel users concerning sustainability. The work of Suanmali [20] is also concerned with detecting the factors that affect tourists’ satisfaction with the Northern Part of Thailand, using an empirical study relating to the environment. In the work of Kaltenborn et al. [21], tourists’ attitudes are investigated concerning the environmental, social and managerial attributes of the Serengeti National Park, concluding with the identification of factors that affect hotel users concerned about the environmental impact of hotel activities. The work of Bruyere [22] aims to identify rural hotel users’ feelings about the benefits of exploiting rural areas for tourism and then suggest how the management of rural hotels can use these findings. Eliam and Trop [23,24] research users’ opinions on activities that affect the environment, identifying the main points which concern travelers. In order to identify the factors that affect Spanish hotel users during their stay, we compiled a set of factors that affect the environment (see Table 1). It is important to identify the environment factors detected by the users of Spanish hotels as these users are already aware of the problems and the impact of tourism on the environment. Therefore, the experiences and feelings of these users when identifying environment factors of the tourism industry are very valuable. It is also useful to remember that the world tourism sector keeps growing [25]. In 2016, 1235 million tourists traveled to foreign countries, which means some 46 million more than the previous year, according to the World Tourism Organization (WTO) [26,27]. This is the seventh consecutive year of increases. However, the growth rate has slowed down as in 2016 the increase was 3.9%, whereas in Int. J. Environ. Res. Public Health 2018, 15, 553 3 of 20 previous years the rise has been around 4.6%. This means that last year there were 300 million foreign tourists more than in 2008 [28–30]. Table 1. Tourism factors and their impact on the environment. Factors Environmental Impact Accumulation of public Stress for the environment and animals Extreme sports Disturb the fauna Animal feeding Changes in wildlife behavior Diving and snorkeling Damage to sea beds Camping/Picnics Erosion of the soil. Damage to vegetation. Noise. Rubbish Hunting and fishing Reduction of species Off-road driving Destruction of soil and vegetation Noise emission Disturb the animals Climbing, hiking Damage to vegetation Cars Running over animals, pollution, noise “Souvenir” collection Interruption of natural processes Collecting wood Deforestation, destruction of habitats Throwing garbage Deterioration of space and danger to local animal and human health Discharge of waste not suitable for water Water pollution, acidity Construction of facilities Loss and division of habitats Construction of electricity pylons Impact on birds in flight Tourism infrastructure Visual impact on fauna, vegetation and aquatic habitat If we look at 2017 WTO world tourism figures, the number of visitors at destinations all around the world shows that these was a large demand for international tourism in the second half of 2017. Worldwide, international tourist arrivals increased by 6% compared to the same semester of the previous year, far surpassing the sustained and constant growth trend of at least 4% observed since 2010 [31]. These figures give the first six months of 2017 the best semi-annual results obtained in the last seven years. The results are related to the strong growth registered in many destinations and the continuing recovery in those that had registered falls in previous years. Of all WTO regions, growth was greatest in the Middle East (+9%), Europe (+8%) and Africa (+8%), followed by Asia and the Pacific (+6%) and then the Americas (+13%) [27]. In addition, the WTO research concludes that the growth of arrivals was driven by the demand for outbound tourism from the main sourcing markets. In particular, Canada, China, the Republic of Korea, Spain (object of our investigation), the United States, France and the United Kingdom, which have continued to report strong expenditure growth of outbound tourism. 1.3. The Context of Hotel Tourism in Spain When studying the state of tourism in Spain, which is the geographical area on which this study is centered, we found that Spain received 75.6 million tourists in 2016. These figures are 10.3% higher than for 2015. In the last month of 2016, Spain was visited by 4 million foreign tourists, a figure that means an advance of 13.3% on the previous year. In Spain, this sector is a very important part of the national economy, contributing to nearly 11% of GDP (Gross Domestic Product) [27]. Tourists have also increased their spending to 77,000 million Euros. Tourists in Spain usually stay for between 4 and 7 nights. The main tourist destination in Spain was Catalonia. With 18 million tourists, it received 4% more visitors than in 2015. Next were the Canary Islands (with 13.3 million and an increase of 13.2%) and Illes Balears (with 13 million which means an increase of 11.9%) [27,28]. To understand the relevance of the analysis of the best hotels in Spain, we can see the results of FRONTUR’s research in Table 2. This shows that the number of tourists using paid accommodation as their main type of accommodation increased by 15.1% annually in December. In this market, hotel accommodation increased by 11.2% and rented housing increased by 49.2%. Unpaid accommodation showed an increase of 9.1%. Tourists housed in family or friends homes increased by 0.3% and those staying in self-owned housing by 42.5% [28]. Int. J. Environ. Res. Public Health 2018, 15, 553 4 of 20 Table 2. International tourists depending on type of accommodation. Monthly Data Accumulated Data Accumulated Data Type of Accommodation Yearly Variation Absolute Value Absolute Value Yearly Variation TOTAL 3.979.713 13.3 75.563.198 10.3 Total paid accommodation (3) 2.841.464 15.1 59.419.138 11.7 —Hotel accommodation 2.299.988 11.2 47.726.623 11.2 —Rented housing 397.010 49.2 8.278.525 8.0 —Other paid accommodation 144.467 6.4 3.413.990 32.1 Unpaid accommodation 1.138.248 9.1 16.144.060 5.3 —Self-owned housing 320.539 42.5 5.039.040 15.7 —Relatives or friends house 704.622 0.3 9.550.942 9.4 —Other unpaid accommodation 113.087 2.4 1.554.079 31.1 In order to calculate the consequences tourism has on the environment it is also useful to see how these travelers arrived. The largest number of tourists to Spain arrived by air transport (December 2016) with almost 3.2 million arrivals, which represents an annual growth of 16.6%. By road, 0.1% more tourists arrived than in December 2015, while 41.3% more entered by rail and 8.8% more through sea ports, as can be seen in Table 3 [22]. Table 3. Arrival of international tourists according to access routes. Monthly Data Accumulated Data Yearly Variation Yearly Variation Access Routes Absolute Value Absolute Value TOTAL 3.979.713 13.3 75.563.198 10.3 Airport 3.197.756 16.6 60.582.406 11.7 Highway 696.586 0.1 13.038.391 4.4 Train 23.038 41.3 364.115 6.2 Puerto 62.332 8.8 1.578.287 9.6 This data emphasizes the importance that tourism has in the Spanish economy. In order to guarantee its future, companies and especially hotels, must identify the parts of their businesses that affect the environment [21,22]. One of the most important factors to be considered is the protection of the environment. This concept includes many points, not only landscapes and natural resources, but is also closely linked to the quality of the services which are offered and is a variable that constantly appears in the tourism sector [22]. This research can therefore be seen to be of interest to the management of hotels, who have to understand that the activities of their hotels and related services affect the environment and world sustainability. Consequently, it is important for them to improve their services by detecting if the hotel users considered that the environment is well treated or not during their stay at the hotel. Hotels should recognize the importance of this sector as a resource that represents the environment, so that it is not harmed in any way and therefore does not affect the current and future economic and social stability, which is a very important sector in the global economy. The future proposal for the tourism sector is based on quality and sustainable tourism which respects the environment [21]. Tourism and recreational activities depend far more than other sectors, on the quality of the natural and cultural environment for their long-term success. In this way, when a country which has attractive areas for tourism becomes an interesting destination for tourism and recreational activities, uncontrolled environmental impacts can jeopardize future benefits [22]. Therefore, tourism can affect the natural environment to the point of endangering its existence, hence the importance of identifying factors that affect the environment as a result of tourism activities in hotels. In this sense, with the developments produced in sectors such as environmental, cultural, economic and social sustainability, this research is concerned with the identification of environment factors from the analysis of the Twitter users’ opinions for the 25 Hotels that won the Traveler ’s Choice Int. J. Environ. Res. Public Health 2018, 15, 553 5 of 20 award from TripAdvisor in 2017. In this exploratory study, Twitter as a means of commenting on a topic, the opinions of hotel users and the identification of key environment factors that affect hotel visitors will be studied. To complete this exploratory study a Sentiment analysis was carried out using an algorithm developed in Python by MonkeyLearn API (MonkeyLearn, San Francisco, CA, USA) [32] with which 14,459 tweets were grouped according to feeling (positive, negative or neutral) and then a textual analysis was performed on these tweets to identify the main environmental factors from the feelings shown using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). Afterwards, a linear correlation analysis and difference between means test were done using IBM SPSS version 24 (IBM, Chicago, IL, USA) and the analysis and results were presented. Finally, a discussions and conclusions section was written for the results obtained. 2. Related Work 2.1. Machine Learning and Sentiment Analysis Approaches for Social Network Analysis There are several researchers that have developed models based on machine learning for the analysis of social networks, the opinions that users have or to identify key factors related to a specific theme [33,34]. Supervised methods based on classification and categorization of key factors such as MaximumEntropy (MaxEnt) and Support Vector Machines (SVMs) are used for a combination of features to perform social network analysis with machine learning and research methods based on technology to identify the important factors of a research category [7,35–37]. These investigations can be based on keywords, ratings of feelings regarding a topic, semantic meaning, concepts and semantic theories, sentiment-topic features such as hashtags, retweets or points on social networks, and valuation identifiers for products and services on the Internet [38,39]. The research of Pak and Paroubek [40] presents an in-depth development of methodologies based on a study of Twitter. In general, semantic approaches based on sentiment analysis determine the occurrence of the key words, to which a statistical factor has been added. This factor determines the feeling [41] and is often used to perform analysis of positive and negative feelings and shows that Twitter can be an appropriate platform for researching factors of interest [42,43]. In the research by Zhang, Yun, Liang y Zhang [44] a semi-supervised dual recurrent neural network is proposed to prepare a Sentiment Analysis. This is similar to traditional neural networks and can be used to evaluate a set of data over a long period of time. This technique allows a more effective and efficient sentiment analysis to be carried out. In Turney y Pantel [45], a recursive neural network is used to understand the meaning of particular comments. To achieve this, sentiment analysis identifies words and offers the semantic meaning for the particular topic of interest [46–48]. Neural networks are used to establish labels for each word and classify them according to predetermined criteria. The research of Robinson [49] proposed a probabilistic model that is called Textual-based Information Diffusion and Evolution (TIDE), with which the evolution of different topics in social communities and their diffusion over time was measured. This method was based on Sentiment Analysis. The model extracts characteristics from the text of the comments made and implicitly captures them using standard ranges of the Gaussian field, as other authors have done [50–53]. Table 4 shows a summary of the main research using Sentiment Analysis and textual analysis combined with other methodologies based on machine learning and semantic analysis. Int. J. Environ. Res. Public Health 2018, 15, 553 6 of 20 Table 4. Characteristics of Sentiment Analysis in investigations. References Characteristics Pak et al. Kuo et al. Honeycutts Kouloumpis Rodríguez-Herráez Boyd et al. This [34] [9] et al. [50] et al. [54] [41] [51] Research p p p p p Neuronal Connection - - p p p Textual analysis - - - - Time - - - - - - p p p Hashtags, URLs or mentions - - - - p p p Topic - - - - p p p Classification of information - - - - 2.2. Textual Analysis Textual analysis is a qualitative and exploratory procedure that determines the key factors of an event or object of study by grouping them into topic nodes. The Nvivo qualitative analysis software is one of the most relevant in this research category and has been used on numerous occasions in the last decade as a research method [55–58]. In Vázquez and Escamilla [52] a qualitative approach using the Nvivo software is taken to determine the attitudes of older people in order to perform a textual analysis on the contents of the results of the research. Ramirez-Andreotta et al. [53] also carry out a textual analysis to determine and understand the short- and long-term impacts of bio-monitoring and exposure of the participants in the study, in order to identify future factors for environmental justice using the Nvivo software. In the work of Saito, Nakano and Kimura [59] a probabilistic matrix for the prediction of re-tweets based on textual analysis was developed. The social context of the relationships between the messages and their time latency were studied. Likewise, Jiang et al. [60] analyzed the fundamental factors that affect a concept called “re-tweetability” of each tweet when using a predictive filter based on collaboration between users. Textual analysis is used to determine and identify the most repeated factors of the study, and, based on these, determine the corresponding actions for the investigation. In Table 5 the research using textual analysis can be seen, along with the main characteristics of qualitative analysis software such as Nvivo, which includes classification into nodes, categorization by topic, number of times a keyword is repeated and the type of keywords that are repeated. Table 5. Characteristics of textual analysis in investigations. References Characteristics Kwon et Ramirez-Andreotta Rosa et al. Honeycutt Boyd et Saito et Jiang et This al. [17] [18] [48] et al. [50] al. [51] al. [52] al. [53] Research p p p p p p p p Classification into nodes p p p p p Categorization - - - p p p p p p Word Count - - p p p Key word - - - - - 3. Conceptual Framework and Hypothesis Development As we have already outlined, the interest of researchers over the last decades has been focused on the identification and analysis of key determinant factors in social networks with regard to a specific topic. Suanmali [24] detects the environment factors that affect the satisfaction of tourists by using an empirical study in the Northern Part of Thailand and links these factors to the actions carried out by hotels in the geographical area in which they are located. Kaltenborn [25] also investigates the attitudes of tourists to the environmental, social and managerial attributes of Serengeti National Park, concluding with the identification of factors that affect the interest of users and then defines and suggests actions that can be taken by the hotel sector to respect and conserve the environment. The environmental actions carried out by hotels are those actions related to the improvement of services based on social policies, the promotion of traditional products or local consumer goods, services related to the quality of the air or the mountain areas where the facilities are Int. J. Environ. Res. Public Health 2018, 15, 553 7 of 20 located, etc. Pak and Paroubek [40] carried out research in which a recursive neural network was used to understand what a particular content says and categorizes it with respect to the identification of specific terms. Pak and Paroubek [40] undertake a sentiment analysis to identify key words and offer the semantic meaning for each of the positive, negative or neutral links [42]. Based on the statements made above, it is proposed that: Hypothesis 1 (H1). The type of consumer experience in Spanish hotels (positive, negative or neutral) influences the environmental actions carried out by hotels. As indicated previously by Bruyere [26], who identified the feelings of users who visited hotels in rural areas and how these influenced the users’ experiences in hotels in those areas. Eliam [27] also investigates user attitudes to the environment and identifies the key factors that were expressed. Likewise, Moreno et al. [12] develops an enriched consumer recruitment system to increase the retention of users in campaigns for community schemes on social networks, classifying the key factors that was shared in the content and relating them to the research objective [61]. The factors that users consider to be relevant to the environment in hotels can determine their satisfaction with the services contracted in the hotel and can modify their enjoyment in a positive or negative way. The work of Roshan et al. [11] focuses on companies in different periods in order to categorize what the most important factors are when assessing the users’ experience with the companies. Roshan et al. [11] evaluate users experiences and opinions, and determine key factors from these [62]. Therefore, the following hypothesis is presented: Hypothesis 2 (H2). The environmental factors that users of Spanish hotels observe during their stays influence their experience at the hotel. 4. Methodology The methodology used was firstly to perform a Sentiment Analysis on Twitter posts [7,33]. Using the results of this Sentiment analysis, a textual analysis was done to identify the key factors related to the environment [44,46]. Finally, bivariate linear correlation was used to demonstrate the statistical significance of the results [63] and then a difference of means test was done on the type of hotels, which were grouped according to their star ratings. This test was based on the Levene Test using the statistical analysis program IBM SPSS version 24. 4.1. Sample As we have already indicated, the objective of this exploratory study is to identify the positive, neutral and negative factors related to the environment that affect the experiences of hotel users. These factors can help hotel management to improve their services and propose new social responsibility strategies for sustainability and respect for the environment such as, tourist activities which respect the environment or the promotion of activities that do not pollute the environment. The research sample is composed of Spanish hotels ranked using TripAdvisor Traveler ’s Choice Awards, which draws from more than 500 million opinions from travelers in Spain. Also, it is important to notice that Spain has become the second most visited country in world (82 million tourists visited Spain in 2018 according to data from the government of Spain). To identify the factors related to the environment, we analyzed the reviews that hotel users made on Twitter between 1 October 2016 and 1 October 2017 for the 25 hotels that won the Traveller ’s Choice Awards. A total of n = 14,459 tweets for the 25 hotels that make up the sample were analyzed [64]. As we have already indicated, the research sample is made up of the Twitter profiles of the 25 hotels that won the TripAdvisor Traveller ’s Choice Awards in 2017 and that have been classified according to: Active and official profile on Twitter Int. J. Environ. Res. Public Health 2018, 15, 553 8 of 20 Public profile Number of opinions in tweets Number of interactions with users Appendix A shows the identification of the hotels under study in TripAdvisor and Twitter. 4.2. Data Collection and Extraction Data was collected using the Twitter API between 1 October 2016 and 1 October 2017. To carry out the Sentiment Analysis, we used the access algorithm in the MonkeyLearn API [32] that is written in Python and uses machine learning techniques to improve the levels of prediction and significance. Firstly, the data was collected and classified using the Twitter API. Then, after having thoroughly trained the algorithm that does the Sentiment Analysis with part of the sample data, the entire database was analysed. The tweets for each hotel were divided into negative, positive and neutral groupings. Thirdly, a textual analysis was performed using the Nvivo 11 software (QSR International, Melbourne, Australia). The tweets were separated into nodes according to the feelings expressed (N , N and N ) 1 2 3 and then thematic nodes were identified to test N , which is made up of the key environment factors. 4.3. Textual Analysis The next step was to perform a textual analysis to identify the environment factors in the analyzed tweets. We used the Nvivo software to do this as it allows us to completely configure the Analysis for our test purpose [22]. The Nvivo software allows for classification by Nodes. The Nodes are configured as containers for the information which includes the evidence and has already been grouped beforehand [65–67]. It must be emphasized that the creation, design and exploration of nodes is a way to research pure data, in order to achieve higher quality descriptive and explanatory levels than could be reached without it [68]. Free nodes are data containers that can group ideas separately and that are not conceptually related to other nodes in an analysis. Branched nodes, are used to represent concepts grouped by categories which are logically linked and can be grouped hierarchically. The analysis results are then presented and are characterized using different indicators [69]. To do this we have established different nodes to identify the main environmental factors from the feelings examined with the nodes [70]. Firstly, a textual analysis was carried out on environmental factors in the tweets, classified as negative feelings (N ), secondly, with the tweets classified as neutral feelings (N ) and thirdly with the tweets classified as positive feelings (N ). 2 3 An exploratory analysis of each of the nodes allowed us to identify environment factors which were grouped into the node (N ) and then divided into three categories. Table 6 shows the classification of the nodes according to negative, neutral and positive environment factors [71]. Table 6. Environmental Node classification with Nvivo software. Factor Identification Nodes Negative Node 1 (N ) Neutral Node 2 (N ) Positive Node 3 (N ) 5. Findings 5.1. Sentiment Analysis In order to identify the environment factors observed by hotel users, the results of the textual analysis were analyzed for each of the nodes [72]. Firstly, the classifications which were used for the Sentiment Analysis carried out with machine learning are presented. These classify all of the users’ opinion tweets into positive, negative and neutral [73]. Int. J. Environ. Res. Public Health 2018, 15, 553 9 of 20 Next, in Table 7, the probability coefficients obtained from the classification of the Sentiment Analysis are presented with a summary of the content averages for each feeling. Then, the results of Int. J. Environ. Res. Public Health 2018, 15, x 9 of 20 the textual analysis are presented for the nodes that subdivide each of the topics and factors that have Next, in Table 7, the probability coefficients obtained from the classification of the Sentiment been identified in the research. Analysis are presented with a summary of the content averages for each feeling. Then, the results of the textual analysis are presented for the nodes that subdivide each of the topics and factors that have Table 7. Environment data which was analyzed and average classification probability percentages for been identified in the research. each Hotel. Table 7. Environment data which was analyzed and average classification probability percentages for Traveller’s Choice from TripAdvisor 2017 Tweets Negative Neutral Positive Average Probability each Hotel. Hotel The Serras 159 1 90 68 0.639 Traveller’s Choice from TripAdvisor 2017 Tweets Negative Neutral Positive Average Probability Vincci Selección Aleysa Hotel Boutique & Spa 1665 81 734 850 0.610 Hotel The Serras 159 1 90 68 0.639 Casa Camper Hotel Barcelona 52 1 39 12 0.555 Vincci Selección Aleysa Hotel Boutique & Spa 1665 81 734 850 0.610 Hotel Orfila 436 1 419 16 0.662 Casa Camper Hotel Barcelona 52 1 39 12 0.555 Hotel Abadia Retuerta Le Domaine 399 15 180 204 0.620 Gran Hotel Son Net 213 - 161 52 0.616 Hotel Orfila 436 1 419 16 0.662 Hotel Maria Cristina 211 10 100 101 0.614 Hotel Abadia Retuerta Le Domaine 399 15 180 204 0.620 Only YOU Boutique Hotel Madrid 756 12 383 361 0.651 Gran Hotel Son Net 213 - 161 52 0.616 Alma Pamplona Muga de Beloso 40 1 16 23 0.674 Hotel Maria Cristina 211 10 100 101 0.614 Seaside Grand Hotel Residencia 128 58 938 726 0.624 Only YOU Boutique Hotel Madrid 756 12 383 361 0.651 Hotel Hacienda de Abajo 80 - 44 37 0.666 Alma Pamplona Muga de Beloso 40 1 16 23 0.674 Hotel Olivia Balmés 35 - 28 8 0.609 Seaside Grand Hotel Residencia 128 58 938 726 0.624 Riviera Beachotel 369 9 225 136 0.682 Hotel Hacienda de Abajo 80 - 44 37 0.666 Sant Francesc Hotel Singular 355 - 296 60 0.598 Hotel Olivia Balmés 35 - 28 8 0.609 El Palace Hotel 373 19 173 182 0.600 Riviera Beachotel 369 9 225 136 0.682 Gran Hotel La Perla 1539 86 670 784 0.607 Sant Francesc Hotel Singular 355 - 296 60 0.598 Barceló Emperatriz 1722 59 938 726 0.624 El Palace Hotel 373 19 173 182 0.600 Hotel Astoria Playa Only Adults 93 1 62 30 0.596 Gran Hotel La Perla 1539 86 670 784 0.607 Catalonia Square 2762 32 1859 935 0.652 Barceló Emperatriz 1722 59 938 726 0.624 H10 Cubik 1407 8 1214 185 0.711 Hotel Astoria Playa Only Adults 93 1 62 30 0.596 Gold By Marina 720 19 398 304 0.622 Catalonia Square 2762 32 1859 935 0.652 Hotel Spa Relais 945 34 437 475 0.679 H10 Cubik 1407 8 1214 185 0.711 n = 14,459 Gold By Marina 720 19 398 304 0.622 Hotel Spa Relais 945 34 437 475 0.679 n = 14,459 The total number of analyzed tweets, n was 14,459. The average of published tweets was 657.22 The total number of analyzed tweets, n was 14,459. The average of published tweets was 657.22 and from the machine learning Sentiment Analysis the greatest probability percentage was 0.679 and and from the machine learning Sentiment Analysis the greatest probability percentage was 0.679 and the least was 0.555 [74]. the least was 0.555 [74]. The probability percentages resulting from the Sentiment Analysis for each tweet classification The probability percentages resulting from the Sentiment Analysis for each tweet classification can be seen in Figure 1. The probability percentage is a measure of accuracy, precision and recall of the can be seen in Figure 1. The probability percentage is a measure of accuracy, precision and recall of samples in each category. the samples in each category. Figure 1. Classification of tweets according to feelings about the environment. Figure 1. Classification of tweets according to feelings about the environment. From all the analyzed tweets, 477 were negative, 7737 were neutral and 6275 were positive, which show that Twitter can be used as a platform to establish a relationship with the user [75]. From all the analyzed tweets, 477 were negative, 7737 were neutral and 6275 were positive, which show that Twitter can be used as a platform to establish a relationship with the user [75]. 5.2. Textual Analysis 5.2. Textual Analysis To identify the key environment factors in these user interactions, we carried out a textual analysis with Nvivo, for which each of the nodes of the textual analysis was divided into positive, To identify the key environment factors in these user interactions, we carried out a textual analysis negative and neutral nodes for the environment factors [76]. with Nvivo, for which each of the nodes of the textual analysis was divided into positive, negative and In Figure 2 below, we can the results of the global textual analysis after the Sentiment Analysis, and the environment factors which were tested [77]. neutral nodes for the environment factors [76]. In Figure 2 below, we can the results of the global textual analysis after the Sentiment Analysis, and the environment factors which were tested [77]. Int. J. Environ. Res. Public Health 2018, 15, 553 10 of 20 Int. J. Environ. Res. Public Health 2018, 15, x 10 of 20 The textual analysis of the environmental factors in N1 found the following results when The textual analysis of the environmental factors in N found the following results when analyzing analyzing the global opinions of users about their experience in the hotel [78]. Table 8 shows the the global opinions of users about their experience in the hotel [78]. Table 8 shows the Semantic analysis Semantic analysis for negative environmental factors and Table 9 shows three negative tweets for negative environmental factors and Table 9 shows three negative tweets examples. examples. Figure 2. Relationship of Nodes and number of tweets for environment factors identification. Figure 2. Relationship of Nodes and number of tweets for environment factors identification. Table 8. Results for N1 for environment factors identification. Table 8. Results for N for environment factors identification. N1 Count Similar Factors Weighted Percentage Rubbish collection 16 rubbish, trash, waste 0.06 Atmospheric contamination 15 contamination, breathing, asthma 0.06 N Count Similar Factors Weighted Percentage Pollution 14 dirty, dirt 0.06 Rubbish collection 16 rubbish, trash, waste 0.06 Atmospheric contamination 15 contamination, breathing, asthma 0.06 Table 9. Results for N2 for environment factors identification. Pollution 14 dirty, dirt 0.06 Negative Tweets 1. Really disgusted by the unhygienic food that @barcelohoteles are serving at the #AllegroIsora really regret Table 9. Results for N for environment factors identification. booking here for 8 days when staff won’t do anything about it. 2. @H10_Hotels we are at your resort in Punta Cana sick with food poisoning management does not care. Negative Tweets Many guests are sick We will never return 3. @El_Felips paid for a upgraded room in Barcelo Lanzarote, so disappointed the room is tired, air con very 1. Really disgusted by the unhygienic food that @barcelohoteles are serving at the #AllegroIsora really regret poor, mould in bathroom and smelling very bad!! The installations and trips to locals places are also bad! booking here for 8 days when staff won’t do anything about it. 2. @H10_Hotels we are at your resort in Punta Cana sick with food poisoning management does not care. The negative factors in N1 show the users’ concerns about garbage collection, air pollution and Many guests are sick We will never return the pollution of the hotels’ environment [76] 3. @El_Felips paid for a upgraded room in Barcelo Lanzarote, so disappointed the room is tired, air con very These results demonstrate the users concerns about negative environment factors. The weighted poor, mould in bathroom and smelling very bad!! The installations and trips to locals places are also bad! percentages do not exceed 0.06 for the negative content, so we can affirm that the hotel users are not highly concerned about these environmental factors [76]. The environment factors for N2, neutral factors, are shown in Table 10 below and Table 11 shows The negative factors in N show the users’ concerns about garbage collection, air pollution and three neutral tweets examples. the pollution of the hotels’ environment [76] Table 10. Results for N2 for environment factors identification. These results demonstrate the users concerns about negative environment factors. The weighted N2 Count Similar Factors Weighted Percentage percentages do not exceed 0.06 for the negative content, so we can affirm that the hotel users are not Local products 250 Streetmarket, markets, local produce 0.40 highly concerned about these environmental factors [76]. Handcraft 71 artisan, craftsmen, pottery 0.11 The environment factors for N , neutral factors, are shown in Table 10 below and Table 11 shows Traditional experience 267 shelters, seminars, monasteries 0.11 Looking after nature 57 rivers, villages, mountains, roads 0.09 three neutral tweets examples. Table 11. Neutral tweets examples. Table 10. Results for N for environment factors identification. Negative Tweets 1. RT @AnneSemonin We love to visit Hotel Sant Francesc for a spot of sunshine and a luxury Anne Semonin N Count Similar Factors Weighted Percentage treatment. Local products 250 Streetmarket, markets, local produce 0.40 2. RT @Stanatic_Ness: A beautiful street with so much of history and tradition #PalmaDeMallorca #Spain Handcraft 71 artisan, craftsmen, pottery 0.11 3. Eating a local fish in #LaPalma @haciendadeabajo-medregal-tastes a bit like tuna, v tasty Traditional experience 67 shelters, seminars, monasteries 0.11 Looking after nature 57 rivers, villages, mountains, roads 0.09 Table 11. Neutral tweets examples. Negative Tweets 1. RT @AnneSemonin We love to visit Hotel Sant Francesc for a spot of sunshine and a luxury Anne Semonin treatment. 2. RT @Stanatic_Ness: A beautiful street with so much of history and tradition #PalmaDeMallorca #Spain 3. Eating a local fish in #LaPalma @haciendadeabajo-medregal-tastes a bit like tuna, v tasty Int. J. Environ. Res. Public Health 2018, 15, 553 11 of 20 The textual analysis of quality of service factors (N ) gave results showing how the users consider the products and services that the hotel makes available for planned trips and specialized offers. Neutral environment factors were found, such as the importance of local products in local markets and shops, crafts from local potteries and artisans, traditional experiences such as visits to shelters, seminars and local monasteries that have not suffered changes over the years and finally a respect for nature, rivers and geographical features when constructing villages, along with the preservation of mountains and local roads. By using weighted percentages for N , it can be seen that users give greater importance to N . The highest weighted percentage for environment factors in the textual analysis was 0.40. Finally, the results of the textual analysis for environment factors of the 6275 tweets that make up the N node can be seen in Table 12. Also, Table 13 shows three neutral tweets examples. Table 12. Results for N for environment factors identification. N Count Similar Factors Weighted Percentage Local Traditions 67 hostels, routes, local products, shows 0.29 Air Quality 32 clarity, pure, sight, breathing, asthma 0.12 Customs 44 dances, traditional production, pastry making, viticulture 0.21 Traditional Ecology 12 saltpans, orchards, eco-products 0.10 Nature 11 oasis, islands, mountain, rivers, trails 0.09 Table 13. Positive tweets examples. Positive Tweets 1. #AQuintadaAuga is a #hotel surrounded by #nature in #SantiagodeCompostela. It is a mandatory stop 2. RT @AmyWorsley85: Always looking for a #sustainable place to stay, like the @Olivia_Balmes hotel in #Barcelona which was great! 3. @barcelohoteles @Bobadilla5GL Congrats! More hotels can be sustainable using local biomass for energy. Please visit us at @conectabioener! A great variety of similar environment factors were found for N . In particular, different contents about local tradition, customs, air quality, traditional ecology and the importance of nature were analyzed and evaluated. The users valued positively the environment factors regarding hostels, routes, local products and shows. In addition, use showed positive relationships with the environments of dances and traditional customs, such as pastry making and viticulture. Another very important factor is the quality of the air, which can be linked to breathing problems, asthma and other diseases [73]. Traditional ecology is represented by factors such as salt pans, orchards and organic products. In general, nature is linked to concepts such as oasis, islands, mountains, rivers and paths. The weighted percentages of the textual analysis of N are between 0.39 and 0.09 in terms of positive factors related to the environment. Table 14 shows all the environmental factors identified as a result of the Sentiment Analysis and subsequent textual analysis of the opinions and tweets of the hotel users that make up the sample. Table 11 summarizes the main environmental factors in the users’ opinions for the hotels of the 2017 Traveler ’s Award and can be used to highlight the most important factors for the consumer. That is to say, the analysis of the results shows how the Spanish hotels users express their attitudes towards the environment factors which influence their experiences. The results of this exploratory study can be used by hotel management to pay attention to the factors which are shown to be important to the guests. The positive factors that affect users visiting Spanish hotels are directly related to the activities carried out or managed by the hotels and which are associated with local traditions, air quality, customs, traditional ecology or nature. Hotel management can therefore organize activities that promote these factors to improve the users’ experience. Int. J. Environ. Res. Public Health 2018, 15, 553 12 of 20 Table 14. Results of N for environment factors identification. Nodes Environment Factors Related Factors Total Count Local Traditions hostels, routes, local products, shows clarity, Air Quality pure, sight, breathing, asthma, dances, Positive Factors Customs traditional production, pastry making, 166 Traditional Ecology viticulture, saltpans, orchards, eco-products, Nature oasis, islands, mountain, rivers, trails Local products street market, markets, local produce Handcraft artisan, craftsmen, pottery Neutral Factors 445 Traditional experience shelters, seminars, monasteries Looking after nature rivers, villages, mountains, roads Garbage collection rubbish, trash, waste Negative Factors Atmospheric pollution contamination, breathing, asthma 45 Pollution dirty, dirt The results also show that the activities provided by the hotels with local and artisan products, traditional experiences and care for nature are factors that are neutral for visitors. However, the results of the exploratory study show that hotel management can use these results to improve their activities, initiatives and services, taking into account that the users regard garbage collection, atmospheric pollution and pollution as negative environment factors. 5.3. Linear Correlation Analysis and Difference between Means Tests In order to show the statistical significances as an additional analysis of the investigation results, a bivariate linear correlation was made to check the possible relationships between the variables. The dependent variables were the number of followers on Twitter, and the negative, neutral or positive feeling of the tweets that were in the results [63]. The independent variables are the number of stars that the hotels under study have on TripAdvisor, the number of comments in the official Twitter profiles and the number of followers [64,65]. This test is used to check the possible relationship between two metric variables. The measurement is carried out using the linear correlation coefficient, which ranges between 1 and 1. If the value is 1 and positive, it indicates variation in the same direction for both variables. A negative value of 1 indicates variation in opposite directions, which means that when one of the variables increases, the other decreases. A value close to zero means that the variables behave independently. Therefore, correlation shows the extent to which two variables share a variation. To calculate the percentage of joint variation, the coefficient of determination is used, which is the square of the linear correlation coefficient. Correlations measure how variables or rank orders are related. The results of the correlations are shown in Table 15. Table 15. Bivariate linear correlation results for environmental factors. Number of Environment Number of Number of Correlation Results Stars Comments Environment Tweets Followers Pearson Correlation 0.220 0.183 0.588 ** 1 Number of Sig. (bilateral) 0.326 0.415 0.006 Followers N 22 22 20 22 Pearson Correlation 0.183 0.325 0.648 ** 0.371 Negative Sig. (bilateral) 0.414 0.140 0.002 0.089 Tweets N 22 22 20 22 Pearson Correlation 0.152 0.339 0.780 ** 0.339 Sig. (bilateral) 0.499 0.123 0.000 0.122 Neutral Tweets N 22 22 20 22 Pearson Correlation 0.068 0.303 0.688 ** 0.381 Positive Tweets Sig. (bilateral) 0.764 0.171 0.001 0.081 N 22 22 20 22 ** Significance intensity. Int. J. Environ. Res. Public Health 2018, 15, 553 13 of 20 High correlation between five variables can be seen: Number of Environment Tweets, Number of Followers, Negative Environment Tweets, Neutral Environment Tweets and Positive Environment Tweets, and also high significance (**) of two-by-two correlation coefficients. This can be between 1 (a perfect negative relationship) and +1 (a perfect positive relationship). A value of 0 indicates that there is no linear relationship. The test for significance was unilateral probability, since the direction of association was Number of Environment Tweets towards the type of Tweets and Number of Followers towards Number of Tweets. Correlations with significant correlation coefficients (p  0.05) are indicated with a single asterisk and those with (p  0.01) are identified by two asterisks. The results show that there are significant correlations (p  0.01) between Number of Tweets and Negative Environment Tweets, Neutral Environment Tweet and Positive Environment Tweets. In all three cases correlation is positive and close to +1. Also, there is significant correlation (p  0.01) between Number of Followers and Number of Environment Tweets. In addition, we can conclude that the higher the number of tweets in a hotel profile, the higher the number of negative tweets (Pearson correlation = 0.648, p  0.01). However, the relationship is even more significant if we take into account that the higher the number of tweets made for the hotel, the higher the number of neutral comments (Pearson correlation = 0.780, p  0.001) and negative comments (Pearson correlation = 0.688; p  0.001). The coefficient of determination (R ) shows that 47% of the changes in the number of Tweets correspond to change in the number of negative tweets. The highest value of R , 60.8%, is obtained by the number of neutral environmental tweets. In addition, a descriptive statistical analysis was carried out using a difference of means test. It is quite frequent in market research to hypothesize whether the differences in behavior detected in two population subsamples give sufficient evidence for differences in the populations or if, on the contrary, they are only a product of sampling error. Before applying the mean difference test or Student’s t test, any statistically significant differences when comparing the averages of the two independent subsamples must be determined. The Levene Test must be performed to check whether the variances are different or not. A predictive model is said to present homoscedasticity when the variance of the error of the endogenous variable is maintained throughout the observations. Therefore, this condition is checked before carrying out the hypothesis test that leads to the t test. To do this, we divided the sample into two groups: 5-star hotels and 4.5-star hotels. As can be seen in Table 16, the difference in means was not significant, except in negative comments (p  0.05), where there are differences and where there are more negative comments in hotels with fewer stars. Table 16. Differences of means test results. Difference of Difference of Variable F Sig. t Means Standard Error Nº comments 1.363 0.257 1.687 510.933 302.795 Tweets 0.066 0.800 0.414 725.000 1750.570 Followers 2.943 0.102 1.006 16,259.150 16,157.287 Negative 7.454 0.013 0.833 9.683 11.620 Neutral 1.483 0.237 0.689 139.617 202.547 Positive 0.932 0.346 0.305 41.933 137.628 F: F Square; Sig.: Significance level; t: t-Student. 6. Discussion As the work of Honeycutt and Herring [50] indicates, the opinions and reviews of users can be analyzed from a consumer satisfaction perspective since concerns and opinions are identified regarding Int. J. Environ. Res. Public Health 2018, 15, 553 14 of 20 the purchase of a product, or use of a service [31,41]. Furthermore Agarwal et al. [35] indicates that an exploratory study can be carried out to link these concepts with any particular research theme [37]. In this way, this exploratory study links the main environment factors for the users who have visited hotels that appear in the Traveler ’s Ranking 2017. The work of Kwon [17] shows Sentiment Analysis as a research process that allows for correct classification of users’ opinions and reviews on social networks and 2.0 platforms. Social networks have given companies new ways to receive the impressions and expectations of their customers. The works of Rosa, Batista and Carvalho [42] and Halog [52] show the need to understand these new phenomena and above all to know how to identify problems and key factors. To do this in our exploratory study, key factors were identified that could help companies in the tourism sector to improve the quality of their services and offer better products [20] based on the improvement of environment factors identified in the investigation [7,13]. As stated, after conducting the Sentiment Analysis, a total of n = 14,459 tweets were identified for the sample of hotel users. These tweets were classified as negative, neutral and positive with an algorithm developed in Python and based on machine learning by MonkeyLearn API [7,26]. As we have already verified from the analysis of the research results by using a qualitative approach on the analyzed environment indicators, Twitter has been shown to be a valid platform for research on the environment. This exploratory study shows that although some Twitter users make complaints or take advantage of offers and discounts, many users comment on the environmental factors which determined their expectations and experiences at hotels. Therefore, this exploratory study demonstrates that it is possible to determine the relationships that hotel users in Spain have with the key environment factors that can be improved or attended to by hotels such as those related to activities carried out by the hotel and the impact of these on the environment, on air quality or on the pollution generated by these activities. This can be seen because the hotel users show interest in these factors in their tweets and so they consequently partially determine the satisfaction and experience that the visitors have when they stay at the hotels. In addition, the quantitative analysis finds that a greater number of followers on Twitter does not mean that these followers are positive in their comments. In fact, the exploratory study shows that negative comments increase when hotels have more followers. This is due to the users who experience negative environmental factors usually follow the hotel’s Twitter profile to make complaints or indicate dissatisfaction with the services provided. Also, as a result of the quantitative analysis we saw that the number of followers on Twitter does not necessarily increase the number of positive comments of the hotel users in the Spanish hotel sector. We also found that if the number of followers is greater, the neutral comments also increase which means that hotel users use Twitter as a source of information for activities, updates and monitoring of hotel communications, but not necessarily to express satisfaction with the hotel. Twitter has therefore consolidated itself as a social network that can be the object of exploratory study related to the environment. It can also help hotel managers to improve their services by identifying the environment factors which travelers have shown interest in during their stay. 7. Conclusions Over the last few decades, the use of new technologies has led to changes in sectors related to environment, culture, sustainability and global warming [1,5]. The development of new technologies and especially social networks has allowed the tourism sector to incorporate important changes when assessing actions related to the environment and social responsibility policies [6,7]. For this investigation, the hotels which won the Traveler ’s Choice Award, which is based on over 500 million travelers’ reviews on TripAdvisor, were used. These hotels have achieved ratings of between 4.5 and 5.0, with 5 being the highest valuation from people who used their services. The findings of this exploratory study were analyzed to find the environment factors which could concern hotel users, with the negative, neutral and positive environment factors being classified as N , 1 Int. J. Environ. Res. Public Health 2018, 15, 553 15 of 20 N and N . The affect that these environment factors have on the users who visit the hotels was also 2 3 studied (N ) [7]. H1 is confirmed because after the textual analysis, it was seen that factors related to the environment influence the experience that users have in hotels, and these users refer to different factors based on their feelings with opinions and reviews of the hotels. H2 is accepted, as shown by the results of the exploratory study, because the N , N and N structures have been developed and 1 2 3 the main factors that affect hotel users have been structured and classified into three types of feelings; negative, neutral and positive. Likewise, H2 has been accepted since the results of the exploratory study show that there are connections between the reviews that users leave about their experiences in hotels on Twitter with the identification of factors related to the environment. The contribution of this exploratory study is the identification of the key environmental concepts that can help the hotel industry to make improvements in the activities and actions related to the environment, significantly improving the perception that users have of topics related to ecology, traditional commerce, ecological products or air quality, among other factors. Hotels can use the results of this exploratory study to highlight the importance that the factors identified by users have for the hotel sector and act upon them. Therefore, the results of this exploratory study are relevant for hotel management because they can be used to add value to the strategies for improvement of tourist activities so that they respect the environment. Some examples such as hotel activities that could affect the garbage collection or atmospheric pollution in its surroundings. Also, improvements can be made in the hotel activities to support and respect local traditions, improve air quality, support traditional customs and ecology and respect and maintain activities that favor the natural environment of the geographical area in which the hotel is located. This exploratory study shows positive and negative concerns about the quality of services when users travel and use the services and activities organized by the hotels. This shows that travelers really do care about the environment and the consequences of their trips. Spanish hotels can also use the results of this exploratory study to develop new strategies for corporate social responsibility and to correctly identify which environmental factors concern users so that hotel activities can be adapted to incorporate actions that support or disseminate local traditions and products, maintain the local environment and wildlife of rural areas, or respecting air quality in the areas where hotels have facilities and offer services. With the results of this exploratory study Spanish hotels can increase user satisfaction by taking into account the environmental factors that were identified in this study. The limitations of this study are related to the number of subjects that make up the sample, the time frame in which the reviews were made and the qualitative analysis carried out to determine the topics of interest, as well as the Occurrence and Reliability percentages of the results. Further studies could be made into the environment factors detected by hotel users and moderating variables such as age or sex, as well as the geographical areas in which the hotels are located. Also, how the users’ feelings about these factors influence their satisfaction with the hotel and its services, along with the real impact they have on the environment. Author Contributions: Jose Ramon Saura, Pedro Palos-Sanchez and Miguel Angel Rios Martin conceived and designed the review; Jose Ramon Saura performed the methodology; Pedro Palos-Sanchez and Miguel Angel Rios Martin analyzed the results; Jose Ramon Saura, Pedro Palos-Sanchez and Miguel Angel Rios Martin wrote the paper. Conflicts of Interest: The authors declare no conflict of interest. Int. J. Environ. Res. Public Health 2018, 15, 553 16 of 20 Appendix Table A1. Winning traveler ’s choice hotels from TripAdvisor sample in 2017. Traveller’s Choice fromTripAdvisor 2017 Location TripAdvisor Starts Customer Reviews Twitter Profile Tweets Followers Hotel The Serras Barcelona, Spain 5.0 843 @TheSerrasHotel 682 727 VincciSelecciónAleysa Hotel Benalmádena, Spain 5.0 842 @Vincci_Hoteles 11.9 k 19.6 K Casa Camper Hotel Barcelona Barcelona, Spain 5.0 1924 @casacamper 1042 175 Hotel Orfila Madrid, Spain 5.0 111 @HotelOrfila 726 356 Hotel AbadiaRetuerta Le Domaine Sardón de Duero, Spain 5.0 449 @arledomaine 2642 1.365 Gran Hotel Son Net Puigpunyent, Spain 4.5 598 @GranHotelSonNet 1260 3286 Hotel Maria Cristina San Sebastián—Donostia, Spain 4.5 1794 @hotelmariacrist 1592 2218 Only YOU Boutique Hotel Madrid Madrid, Spain 5.0 1975 @OnlyYOUHotels 4512 3877 Alma Pamplona Muga de Beloso Pamplona, Spain 5.0 1187 @almahotels 68 86 La Bobadilla, a Royal Hideaway Hotel Loja, Spain 5.0 774 @barcelohoteles 11.2 k 179 K Seaside Grand Hotel Residencia Maspalomas, Spain 5.0 653 @GrandHotelPunta 753 858 Hotel Hacienda de Abajo Tazacorte, Spain 5.0 259 @HaciendadeAbajo 585 712 Hotel Olivia Balmés Barcelona, Spain 4.5 2304 @Olivia_Balmes 548 7241 Riviera Beachotel Benidorm, Spain 4.5 2314 @benidormhoteles 2912 1859 Sant Francesc Hotel Singular Palma de Mallorca, Spain 4.5 476 @hotelstfrancesc 1490 1409 El Palace Hotel Barcelona, Spain 4.5 1776 @ElPalaceHotel 2152 2512 Gran Hotel La Perla Pamplona, Spain 5.0 336 @Ghotellaperla 6440 3020 Barceló Emperatriz Madrid, Spain 4.5 568 @barcelohoteles 11.2 k 179 K Hotel Astoria Playa OnlyAdults Port d’Alcudia, Spain 4.5 2427 @astoriaplayasup 254 139 Catalonia Square Barcelona, Spain 4.5 932 @CataloniaHotels 22.9 k 13.6 K H10 Cubik Barcelona, Spain 4.5 822 @h10hotels 10.9 k 18.4 K Gold By Marina Playa del Inglés, Spain 4.5 1596 @GoldByMarina 1.594 381 Hotel Spa Relais & ChateauxAuga Santiago de Compostela, Spain 4.5 819 @aquintadaauga 4292 1996 Alma Barcelona Barcelona, Spain 4.5 1876 @almahotels 68 86 Hotel Las Madrigueras Golf Resort and Spa Playa de las Américas, Spain 5.0 305 - - - Int. 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Journal

International Journal of Environmental Research and Public HealthPubmed Central

Published: Mar 19, 2018

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