Faculty Publications
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Item Influence factor based opinion mining of Twitter data using supervised learning(2014) Anjaria, M.; Guddeti, G.R.M.Social Networking portals have been widely used for expressing opinions in the public domain through internet based text messages and images. Among popular social networking portals, Twitter has been the point of attraction to several researchers in important areas like prediction of democratic electoral events, consumer brands, movie box office, stock market, popularity of celebrities etc. Sentiment analysis over Twitter offers a fast and efficient way of monitoring the public sentiment. In this paper, we introduce the novel approach of exploiting the user influence factor in order to predict the outcome of an election result. We also propose a hybrid approach of extracting opinion using direct and indirect features of Twitter data based on Support Vector Machines (SVM), Naive Bayes, Maximum Entropy and Artificial Neural Networks based supervised classifiers. We combined Principal Component Analysis (PCA) with SVM in an attempt to perform dimensionality reduction. This paper shows two different case studies of entirely different social scenarios, US Presidential Elections 2012 and Karnataka Assembly Elections 2013. We conclude the conditions under which Twitter may fail or succeed in predicting the outcome of elections. Experimental results demonstrate that Support Vector Machines outperform all other classifiers with maximum successful prediction accuracy of 88% in case of US Presidential Elections held in November 2012 and maximum prediction accuracy of 58% in case of Karnataka State Assembly Elections held in May 2013. © 2014 IEEE.Item A novel sentiment analysis of social networks using supervised learning(Springer-Verlag Wien michaela.bolli@springer.at, 2014) Anjaria, M.; Guddeti, R.M.R.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. © 2014, Springer-Verlag Wien.