Approaches for Sentiment Analysis on Twitter: A State-of-Art study

Microbloging is an extremely prevalent broadcast medium amidst the Internet fraternity these days. People share their opinions and sentiments about variety of subjects like products, news, institutions, etc., every day on microbloging websites. Sentiment analysis plays a key role in prediction systems, opinion mining systems, etc. Twitter, one of the microbloging platforms allows a limit of 140 characters to its users. This restriction stimulates users to be very concise about their opinion and twitter an ocean of sentiments to analyze. Twitter also provides developer friendly streaming API for data retrieval purpose allowing the analyst to search real time tweets from various users. In this paper, we discuss the state-of-art of the works which are focused on Twitter, the online social network platform, for sentiment analysis. We survey various lexical, machine learning and hybrid approaches for sentiment analysis on Twitter.

[1]  B. Pang,et al.  Mining Sentiment Classification from Political Web Logs , 2006 .

[2]  Patrick Paroubek,et al.  Twitter Based System: Using Twitter for Disambiguating Sentiment Ambiguous Adjectives , 2010, *SEMEVAL.

[3]  Suhaas Prasad.  Micro-blogging Sentiment Analysis Using Bayesian Classification Methods , 2010 .

[4]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[5]  Akshay Java.  A Framework for Modeling Influence, Opinions and Structure in Social Media , 2007, AAAI.

[6]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[7]  Philip S. Yu,et al.  Text Classification by Labeling Words , 2004, AAAI.

[8]  Prem Melville,et al.  Sentiment analysis of blogs by combining lexical knowledge with text classification , 2009, KDD.

[9]  M. de Rijke,et al.  UvA-DARE ( Digital Academic Repository ) Using WordNet to measure semantic orientations of adjectives , 2004 .

[10]  Bo Pang,et al.  Using Very Simple Statistics for Review Search: An Exploration , 2008, COLING.

[11]  Michael L. Littman,et al.  Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.

[12]  Sabine Bergler,et al.  All Blogs Are Not Made Equal: Exploring Genre Differences in Sentiment Tagging of Blogs , 2007, ICWSM.

[13]  Christopher D. Manning,et al.  Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger , 2000, EMNLP.

[14]  Janyce Wiebe,et al.  Effects of Adjective Orientation and Gradability on Sentence Subjectivity , 2000, COLING.

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

[16]  Steven Skiena,et al.  Large-Scale Sentiment Analysis for News and Blogs (system demonstration) , 2007, ICWSM.

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

[18]  Steven Skiena,et al.  International Sentiment Analysis for News and Blogs , 2021, ICWSM.

[19]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

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

[21]  Alistair Kennedy,et al.  SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS , 2006, Comput. Intell..

[22]  Alistair Kennedy,et al.  Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters , 2005 .

[23]  Rudy Prabowo,et al.  Sentiment analysis: A combined approach , 2009, J. Informetrics.

[24]  Grzegorz Kondrak,et al.  A Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs , 2008, Canadian Conference on AI.