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A Practical Guide to Sentiment AnalysisGenerative Models for Sentiment Analysis and Opinion Mining

A Practical Guide to Sentiment Analysis: Generative Models for Sentiment Analysis and Opinion Mining [This chapter provides a survey of recent work on using generative models for sentiment analysis and opinion mining. Generative models attempt to model the joint distribution of all the relevant data with parameters that can be interpreted as reflecting latent structures or properties in the data. As a result of fitting such a model to the observed data, we can obtain an estimate of these parameters, thus “revealing” the latent structures or properties of the data to be analyzed. Such models have already been widely used for analyzing latent topics in text data. Some of the models have been extended to model both topics and sentiment of a topic, thus enabling sentiment analysis at the topic level. Moreover, new generative models have also been developed to model both opinionated text data and their companion numerical sentiment ratings, enabling deeper analysis of sentiment and opinions to not only obtain subtopic-level sentiment but also latent relative weights on different subtopics. These generative models are general and robust and require no or little human effort in model estimation. Thus they can be applied broadly to perform sentiment analysis and opinion mining on any text data in any natural language.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Practical Guide to Sentiment AnalysisGenerative Models for Sentiment Analysis and Opinion Mining

Part of the Socio-Affective Computing Book Series (volume 5)
Editors: Cambria, Erik; Das, Dipankar; Bandyopadhyay, Sivaji; Feraco, Antonio
Wang, Hongning; Zhai, ChengXiang
Springer Journals — Apr 12, 2017
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27 pages

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG 2017. Chapter 4 is published with kind permission of the Her Majesty the Queen Right of Canada.
ISBN
978-3-319-55392-4
Pages
107 –134
DOI
10.1007/978-3-319-55394-8_6
Publisher site
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Abstract

[This chapter provides a survey of recent work on using generative models for sentiment analysis and opinion mining. Generative models attempt to model the joint distribution of all the relevant data with parameters that can be interpreted as reflecting latent structures or properties in the data. As a result of fitting such a model to the observed data, we can obtain an estimate of these parameters, thus “revealing” the latent structures or properties of the data to be analyzed. Such models have already been widely used for analyzing latent topics in text data. Some of the models have been extended to model both topics and sentiment of a topic, thus enabling sentiment analysis at the topic level. Moreover, new generative models have also been developed to model both opinionated text data and their companion numerical sentiment ratings, enabling deeper analysis of sentiment and opinions to not only obtain subtopic-level sentiment but also latent relative weights on different subtopics. These generative models are general and robust and require no or little human effort in model estimation. Thus they can be applied broadly to perform sentiment analysis and opinion mining on any text data in any natural language.]

Published: Apr 12, 2017

Keywords: Generative model; Probabilistic topic model; Topic-sentiment mixture; Latent aspect rating analysis; Latent variable analysis

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