A Comparative Study on Twitter Sentiment Analysis: Which Features are Good?

In this paper, investigations of Sentiment Analysis over a well-known Social Media Twitter were done. As literatures show that some works related to Twitter Sentiment Analysis have been done and delivered interesting idea of features, but there is no a comparative study that shows the best features in performing Sentiment Analysis. In total we used 9 feature sets (41 attributes) that comprise punctuation, lexical, part of speech, emoticon, SentiWord lexicon, AFINN-lexicon, Opinion lexicon, Senti-Strength method, and Emotion lexicon. Feature analysis was done by conducting supervised classification for each feature sets and continued with feature selection in subjectivity and polarity domain. By using four different datasets, the results reveal that AFINN lexicon and Senti-Strength method are the best current approaches to perform Twitter Sentiment Analysis.

[1]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[2]  Wessel Kraaij,et al.  A Shallow Approach to Subjectivity Classification , 2008, ICWSM.

[3]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[4]  Mike Thelwall,et al.  Sentiment strength detection for the social web , 2012, J. Assoc. Inf. Sci. Technol..

[5]  R. Plutchik.  The psychology and biology of emotion , 1994 .

[6]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[7]  M. Bradley,et al.  Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings , 1999 .

[8]  Marcelo Mendoza,et al.  Combining strengths, emotions and polarities for boosting Twitter sentiment analysis , 2013, WISDOM '13.

[9]  Saif Mohammad,et al.  CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..

[10]  George Papadakis,et al.  Content vs. context for sentiment analysis: a comparative analysis over microblogs , 2012, HT '12.

[11]  Jason Baldridge,et al.  Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph , 2011, ULNLP@EMNLP.

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

[13]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[14]  Finn Årup Nielsen,et al.  A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.

[15]  P. Ekman.  An argument for basic emotions , 1992 .