TJP: Identifying the Polarity of Tweets from Contexts

The TJP system is presented, which participated in SemEval 2014 Task 9, Part A: Contextual Polarity Disambiguation. Our system is ‘constrained’, using only data provided by the organizers. The goal of this task is to identify whether marking contexts are positive, negative or neutral. Our system uses a support vector machine, with extensive pre-processing and achieved an overall F-score of 81.96%.

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

[2]  Markus Zanker,et al.  Classification of Customer Reviews based on Sentiment Analysis , 2012, ENTER.

[3]  Peter D. Turney,et al.  Automatic Dream Sentiment Analysis , 2006 .

[4]  Sameena Shah,et al.  Stock Prediction Using Event-Based Sentiment Analysis , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[5]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[6]  Jeremy Ellman,et al.  TJP: Using Twitter to Analyze the Polarity of Contexts , 2013, *SEMEVAL.

[7]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[8]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[9]  I. C. Mogotsi,et al.  Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze: Introduction to information retrieval , 2010, Information Retrieval.

[10]  WagnerWiebke Steven Bird, Ewan Klein and Edward Loper , 2010, LREC 2010.

[11]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[12]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[13]  Mizuki Morita,et al.  Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter , 2011, EMNLP.

[14]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

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

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

[17]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[18]  Johan Bollen,et al.  Twitter Mood as a Stock Market Predictor , 2011, Computer.

[19]  Preslav Nakov,et al.  SemEval-2014 Task 9: Sentiment Analysis in Twitter , 2014, *SEMEVAL.

[20]  Bernhard Schölkopf,et al.  Support Vector Machines , 2014 .