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Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-981-13-7474-6

ISBN: 978-981-13-7473-9

Publisher: Springer Singapore - Springer Journals

Apr 7, 2019

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Introduction

Chaudhuri, Arindam

Pages: 1 –8

[Sentiment analysis analyses people’s viewpoints, feelings, assessments, behaviour and psychology towards living and abstract entities. It highlights viewpoints which present positively or negatively biased sentiments.]
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Current State of Art

Chaudhuri, Arindam

Pages: 9 –14

[There has been a wide array of domains ranging from fast-moving consumer products to political events where sentiment analysis has numerous applications. Several large companies have their own in-built capabilities in this area. These innumerable applications and interests have been the driving...
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Literature Review

Chaudhuri, Arindam

Pages: 15 –19

[The analysis of sentiments has been a popular research topic towards social media data processing (Dashtipour et al. in Cogn Comput 8(4):757–771, 2016, [1]). The majority of sentiment analysis research is using the English language, but there is a gradual increase towards the multilingual aspect.]
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Experimental Data Utilized

Chaudhuri, Arindam

Pages: 21 –22

[This chapter highlights several social network datasets that are utilized in performing the experiments. Here, the experimental datasets are taken from four social network sites, viz. Twitter, Instagram, Viber and Snapchat.]
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Visual and Text Sentiment Analysis

Chaudhuri, Arindam

Pages: 23 –24

[he information on text has been analysed rigorously in several areas pertaining to business decision-making (Miller et al. in Sentiment flow through hyperlink networks, pp 550–553, 2011, [1]). A tweet for images is shown in Fig. 5.1. The visual information analysis covering information retrieval...
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Experimental Setup: Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks

Chaudhuri, Arindam

Pages: 25 –49

[The experimental setup consists of performing visual and text sentiment analysis through hierarchical based deep learning networks. A brief discussion on the deep learning networks is presented for the interested readers. The cross-media bag-of-words model (CBM) is used as the baseline method....
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Experimental Results

Chaudhuri, Arindam

Pages: 51 –65

[The experimental results are highlighted in this chapter using Twitter, Instagram, Viber and Snapchat datasets. HGFRNN is evaluated through 2-class (+ve, −ve) as well as 3-class (+ve, −ve, unbiased) propositions.]
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Conclusion

Chaudhuri, Arindam

Pages: 67 –68

[In this research, a novel hierarchical GFRNN-based model for analysing sentiments on multimodal content is presented. Giving due consideration for leveraging huge volume of blog contents available towards sentiment analysis, multimodal techniques are utilized here. The learning algorithm of...

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