A Survey on Sentiment Analysis and Opinion Mining Techniques

Sentiment Analysis (SA), an application of Natural Language processing (NLP), has been witnessed a blooming interest over the past decade. It is also known as opinion mining, mood extraction and emotion analysis. The basic in opinion mining is classifying the polarity of text in terms of positive (good), negative (bad) or neutral (surprise). Mood Extraction automates the decision making performed by human. It is the important aspect for capturing public opinion about product preferences, marketing campaigns, political movements, social events and company strategies. In addition to sentiment analysis for English and other European languages, this task is applied on various Indian languages like Bengali, Hindi, Telugu and Malayalam. This paper describes the survey on main approaches for performing sentiment extraction.

[1]  Eric K. Ringger,et al.  Pulse: Mining Customer Opinions from Free Text , 2005, IDA.

[2]  Kerstin Denecke,et al.  Are SentiWordNet scores suited for multi-domain sentiment classification? , 2009, 2009 Fourth International Conference on Digital Information Management.

[3]  Sivaji Bandyopadhyay,et al.  SentiWordNet for Indian Languages , 2010 .

[4]  Pushpak Bhattacharyya,et al.  A Fall-back Strategy for Sentiment Analysis in Hindi: a Case Study , 2010 .

[5]  Marshall S. Smith,et al.  The general inquirer: A computer approach to content analysis. , 1967 .

[6]  Michael Gamon,et al.  Automatic Identification of Sentiment Vocabulary: Exploiting Low Association with Known Sentiment Terms , 2005, ACL 2005.

[7]  Erik Cambria,et al.  Taking Refuge in Your Personal Sentic Corner , 2011 .

[8]  Jun Zhao,et al.  Adding Redundant Features for CRFs-based Sentence Sentiment Classification , 2008, EMNLP.

[9]  Janyce Wiebe,et al.  Learning Subjective Adjectives from Corpora , 2000, AAAI/IAAI.

[10]  J. William Ahwood,et al.  CLASSIFICATION , 1931, Foundations of Familiar Language.

[11]  Peter D. Turney,et al.  Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon , 2010, HLT-NAACL 2010.

[12]  Sivaji Bandyopadhyay,et al.  Subjectivity Detection in English and Bengali: A CRF-based Approach , 2009 .

[13]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[14]  J. Wiebe.  Recognizing subjective sentences: a computational investigation of narrative text , 1990 .

[15]  Jonathon Read,et al.  Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification , 2005, ACL.

[16]  Philip J. Stone,et al.  Extracting Information. (Book Reviews: The General Inquirer. A Computer Approach to Content Analysis) , 1967 .

[17]  Saif Mohammad,et al.  Generating High-Coverage Semantic Orientation Lexicons From Overtly Marked Words and a Thesaurus , 2009, EMNLP.

[18]  Gérard Dray,et al.  Opinion Mining From Blogs , 2009, CISIM 2009.

[19]  Songbo Tan,et al.  A survey on sentiment detection of reviews , 2009, Expert Syst. Appl..

[20]  Vasileios Hatzivassiloglou,et al.  Predicting the Semantic Orientation of Adjectives , 1997, ACL.

[21]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[22]  Hsinchun Chen,et al.  AI and Opinion Mining , 2010, IEEE Intelligent Systems.

[23]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

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

[25]  Dipankar Das,et al.  Labeling Emotion in Bengali Blog Corpus – A Fine Grained Tagging at Sentence Level , 2010 .

[26]  Bing Liu.  Sentiment Analysis , 2020 .

[27]  N. Mohandas,et al.  Domain Specific Sentence Level Mood Extraction from Malayalam Text , 2012, 2012 International Conference on Advances in Computing and Communications.

[28]  Delip Rao,et al.  Semi-Supervised Polarity Lexicon Induction , 2009, EACL.