Approaches to Sentiment Analysis on Product Reviews

Purchase decisions are better when opinions/reviews about products are considered. Similarly, reviewing customer feedback help in improving the sale and ultimately benefit the business. Web 2.0 provides various platforms such as Twitter, Facebook, etc. where one can comment, review, or post to express his/ her happiness, anger, disbelief, sadness toward products, people, etc. To computationally analyze the sentiments in text requires a better understanding of the technologies used in sentiment analysis. This chapter gives a comprehensive understanding about the techniques used in sentiment analysis. Machine learning approaches are mostly used for sentiment analysis. Whereas, as per the text and required results, lexicon-based approaches are also used for the same purpose. This chapter includes the discussion on the evaluation parameters for the sentiment analysis. This chapter would also highlight ontology approach for sentiment analysis and outstanding contributions made in this field.

[1]  Hiroshi Kanayama,et al.  Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis , 2006, EMNLP.

[2]  Preslav Nakov,et al.  SemEval-2013 Task 2: Sentiment Analysis in Twitter , 2013, *SEMEVAL.

[3]  M. F. Porter,et al.  An algorithm for suffix stripping , 1997 .

[4]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[5]  Renata Vieira,et al.  Ontology based feature level opinion mining for portuguese reviews , 2013, WWW '13 Companion.

[6]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[7]  Kathleen R. McKeown,et al.  Predicting the semantic orientation of adjectives , 1997 .

[8]  Ling Liu,et al.  Manipulation of online reviews: An analysis of ratings, readability, and sentiments , 2012, Decis. Support Syst..

[9]  Jan Smid,et al.  Ontology Design with Formal Concept Analysis , 2004, CLA.

[10]  Yung-Ming Li,et al.  Deriving market intelligence from microblogs , 2013, Decis. Support Syst..

[11]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[12]  Eric Brill,et al.  A Simple Rule-Based Part of Speech Tagger , 1992, HLT.

[13]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[14]  Rudolf Wille,et al.  Formal Concept Analysis as Mathematical Theory of Concepts and Concept Hierarchies , 2005, Formal Concept Analysis.

[15]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[16]  Seong Joon Yoo,et al.  Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews , 2012, Expert Syst. Appl..

[17]  Isa Maks,et al.  A lexicon model for deep sentiment analysis and opinion mining applications , 2012, Decis. Support Syst..

[18]  Fabio Crestani,et al.  Like It or Not , 2016, ACM Comput. Surv..

[19]  Walaa Medhat,et al.  Sentiment analysis algorithms and applications: A survey , 2014 .

[20]  Hua Xu,et al.  Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis , 2012, Expert Syst. Appl..

[21]  Nick Bassiliades,et al.  Ontology-based sentiment analysis of twitter posts , 2013, Expert Syst. Appl..

[22]  Chun Chen,et al.  DASA: Dissatisfaction-oriented Advertising based on Sentiment Analysis , 2010, Expert Syst. Appl..

[23]  Chien Chin Chen,et al.  Quality evaluation of product reviews using an information quality framework , 2011, Decis. Support Syst..