A novel sentiment analysis of social networks using supervised learning

Online microblog-based social networks have been used for expressing public opinions through short messages. Among popular microblogs, Twitter has attracted the attention of several researchers in areas like predicting the consumer brands, democratic electoral events, movie box office, popularity of celebrities, the stock market, etc. Sentiment analysis over a Twitter-based social network offers a fast and efficient way of monitoring the public sentiment. This paper studies the sentiment prediction task over Twitter using machine-learning techniques, with the consideration of Twitter-specific social network structure such as retweet. We also concentrate on finding both direct and extended terms related to the event and thereby understanding its effect. We employed supervised machine-learning techniques such as support vector machines (SVM), Naive Bayes, maximum entropy and artificial neural networks to classify the Twitter data using unigram, bigram and unigram + bigram (hybrid) feature extraction model for the case study of US Presidential Elections 2012 and Karnataka State Assembly Elections (India) 2013. Further, we combined the results of sentiment analysis with the influence factor generated from the retweet count to improve the prediction accuracy of the task. Experimental results demonstrate that SVM outperforms all other classifiers with maximum accuracy of 88 % in predicting the outcome of US Elections 2012, and 68 % for Indian State Assembly Elections 2013.

[1]  Ari Rappoport,et al.  Enhanced Sentiment Learning Using Twitter Hashtags and Smileys , 2010, COLING.

[2]  Panagiotis Takis Metaxas,et al.  Limits of Electoral Predictions Using Twitter , 2011, ICWSM.

[3]  Kuan-Ju Chen,et al.  Congressional Candidates’ Use of Twitter During the 2010 Midterm Elections: A Wasted Opportunity? , 2011 .

[4]  Ravi Parikh,et al.  Sentiment Analysis of User-Generated Twitter Updates using Various Classification Techniques , 2009 .

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

[6]  Lucila Ohno-Machado,et al.  Natural language processing: an introduction , 2011, J. Am. Medical Informatics Assoc..

[7]  Ioannis Katakis,et al.  Social Voting Advice Applications—Definitions, Challenges, Datasets and Evaluation , 2014, IEEE Transactions on Cybernetics.

[8]  Matthew Crosby,et al.  Association for the Advancement of Artificial Intelligence , 2014 .

[9]  Jennifer Foster,et al.  Sentiment Analysis of Political Tweets: Towards an Accurate Classifier , 2013 .

[10]  João Francisco Valiati,et al.  Document-level sentiment classification: An empirical comparison between SVM and ANN , 2013, Expert Syst. Appl..

[11]  Long Jiang,et al.  User-level sentiment analysis incorporating social networks , 2011, KDD.

[12]  Huang Zou,et al.  Sentiment Classification Using Machine Learning Techniques with Syntax Features , 2015, 2015 International Conference on Computational Science and Computational Intelligence (CSCI).

[13]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[14]  Lada A. Adamic,et al.  The Party Is Over Here: Structure and Content in the 2010 Election , 2011, ICWSM.

[15]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[16]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[17]  Lyric Doshi,et al.  Using sentiment and social network analyses to predict opening-movie box-office success , 2010 .

[18]  Junlan Feng,et al.  Robust Sentiment Detection on Twitter from Biased and Noisy Data , 2010, COLING.

[19]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

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

[21]  Andrew McCallum,et al.  Using Maximum Entropy for Text Classification , 1999 .

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

[23]  Padmini Srinivasan,et al.  GOP primary season on twitter: "popular" political sentiment in social media , 2013, WSDM.

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

[25]  Min Song,et al.  RT^2M: Real-Time Twitter Trend Mining System , 2013, 2013 International Conference on Social Intelligence and Technology.

[26]  Brendan T. O'Connor,et al.  Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics , 2011 .

[27]  L BergerAdam,et al.  A maximum entropy approach to natural language processing , 1996 .

[28]  Ee-Peng Lim,et al.  Tweets and Votes: A Study of the 2011 Singapore General Election , 2012, 2012 45th Hawaii International Conference on System Sciences.

[29]  Tiejun Zhao,et al.  Target-dependent Twitter Sentiment Classification , 2011, ACL.

[30]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[31]  Stefan Stieglitz,et al.  Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior , 2012, 2012 45th Hawaii International Conference on System Sciences.

[32]  Farhad Nooralahzadeh,et al.  2012 Presidential Elections on Twitter -- An Analysis of How the US and French Election were Reflected in Tweets , 2013, 2013 19th International Conference on Control Systems and Computer Science.

[33]  Long-Sheng Chen,et al.  Journal of Informetrics , 2022 .

[34]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[35]  Bo Pang,et al.  Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.

[36]  Geoff Holmes,et al.  Multinomial Naive Bayes for Text Categorization Revisited , 2004, Australian Conference on Artificial Intelligence.

[37]  Karolin Kappler,et al.  Communication dynamics in twitter during political campaigns: The case of the 2011 Spanish national election , 2013 .

[38]  Johan Bollen,et al.  Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena , 2009, ICWSM.

[39]  A. Smeaton,et al.  On Using Twitter to Monitor Political Sentiment and Predict Election Results , 2011 .

[40]  Zhenyu Yang,et al.  Sentiment analysis on tweets for social events , 2013, Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[41]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.