Sentiment Recognition from Bangla Text

Sentiment analysis is a very important area of the natural language processing. In general, sentiment classification means the analysis to determine the expression of a speaker whether he or she holds positive or negative opinion to a specific subject. With the rapid growth of e-commerce, sentiment analysis can greatly influence everyone in their real life. For example, product reviews on the Web have become an important source of information for customers’ decision making when they want to buy any product. As the reviews are often too many for customers to go through, how to automatically classify and detect the sentiment from them has become an important research problem. In this chapter, the authors present a Sentiment Analyzer that recognizes the Bangla sentiment or opinion about a subject from Bangla text. They construct some phrase patterns and calculate their sentiment orientation. They add tags to words in the Bangla text to construct the phrase pattern for positive and negative sentiment. Then the authors match the phrase pattern in Bangla text with their predefined phrase pattern and cumulate the sentiment orientation of each sentence. Sentiment Recognition from Bangla Text

[1]  Carlo Strapparava,et al.  Developing Affective Lexical Resources , 2004, PsychNology J..

[2]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[3]  Dipankar Das,et al.  Emotion Tracking on Blogs - A Case Study for Bengali , 2012, IEA/AIE.

[4]  Henry Lieberman,et al.  A model of textual affect sensing using real-world knowledge , 2003, IUI '03.

[5]  Jeonghee Yi,et al.  Sentiment analysis: capturing favorability using natural language processing , 2003, K-CAP '03.

[6]  John Yen,et al.  Get Online Support, Feel Better -- Sentiment Analysis and Dynamics in an Online Cancer Survivor Community , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[7]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[8]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[9]  Soo-Min Kim,et al.  Identifying and Analyzing Judgment Opinions , 2006, NAACL.

[10]  Vincent Ng,et al.  Mine the Easy, Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification , 2009, ACL.

[11]  Andrea Esuli,et al.  Determining the semantic orientation of terms through gloss analysis , 2005, CIKM 2005.

[12]  Pero Subasic,et al.  Affect analysis of text using fuzzy semantic typing , 2001, IEEE Trans. Fuzzy Syst..

[13]  Lie Lu,et al.  Automatic mood detection and tracking of music audio signals , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[14]  Steven Skiena,et al.  Empath: A framework for evaluating entity-level sentiment analysis , 2011, 2011 8th International Conference & Expo on Emerging Technologies for a Smarter World.

[15]  Ming Xu,et al.  Feature-level sentiment analysis for Chinese product reviews , 2011, 2011 3rd International Conference on Computer Research and Development.

[16]  Claire Cardie,et al.  Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.

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

[18]  Mitsuru Ishizuka,et al.  Assessing Sentiment of Text by Semantic Dependency and Contextual Valence Analysis , 2007, ACII.

[19]  Michael Siering,et al.  "Boom" or "Ruin"--Does It Make a Difference? Using Text Mining and Sentiment Analysis to Support Intraday Investment Decisions , 2012, 2012 45th Hawaii International Conference on System Sciences.

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

[21]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[23]  Dipankar Das.  Analysis and tracking of emotions in english and bengali texts: a computational approach , 2011, WWW.

[24]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[25]  J. Pennebaker,et al.  Psychological aspects of natural language. use: our words, our selves. , 2003, Annual review of psychology.

[26]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

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

[28]  Vincent Ng,et al.  Topic-wise, Sentiment-wise, or Otherwise? Identifying the Hidden Dimension for Unsupervised Text Classification , 2009, EMNLP.

[29]  Donald A. Adjeroh,et al.  Proximity-based sentiment analysis , 2011, Fourth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2011).

[30]  Nigel Collier,et al.  Sentiment Analysis using Support Vector Machines with Diverse Information Sources , 2004, EMNLP.

[31]  Jian Liu,et al.  Sentiment classification using phrase patterns , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[32]  P. Ekman.  Facial expression and emotion. , 1993, The American psychologist.