User Classification in Twitter Based on Sentiment Analysis on user Tweets

IJCSEC Front Page

This work user classification in twitter deals with classification of the twitter users based on their tweets till dated. Each tweets and re tweets of each users are taken and sentiment analysis is performed .By aggregating the sentiment score of whole tweets of the user his total user score is calculated based on the user score users are classified into different groups like highly positive neutral positive highly negative neutral negative .users who came under negative side will be warned first about their negative behaviour and if they continue they will be blocked.

Keywords: Tweets, Sentiment Analysis, user classification, text emotions, opinion mining, user score.


  1. Shilpa P, Madhu Kumar S D. Feature Oriented Sentiment Analysis in Social Networking Sites to Track Malicious Campaigners", 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS) 10-12 December, 2015
  2. Rui Xia,FengXu, Zong, and Qianmu Li. Dual Sentiment Analysis: Considering Two Sidesof One Review . IEEE TRANSACTIONS 2015.
  3. Andrea Esuli and Fabrizio Sebastiani. Sentiwordnet: A Publicly Available Lexical Resource for Opinion Mining. In Proceedings of 5th Conference onLanguage Resources and Evaluation, 78, pages 417-422. AIAA, 2006.
  4. Alaa Hamouda and Mohamed Rohaim. Reviews Classification Using Sentiwordnet Lexicon. The Online Journal on Computer Science and Information, 2:492 -524, 1997.
  5. Luciano Barbosa and Junlan Feng. Robust Sentiment Detection on Twitter from biased and noisy data. In Proceedings of the 23rd International Conference on Computational Linguistics,pages 36{44, 2010.
  6. Brendon O'Connor and Balasubramanyan From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. In InternationalConference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Feb 2014.
  7. J Piskorski and Frontex. Exploiting Twitter for Border Security-Related Intelligence Gathering. In IEEE European Intelligence and Security Informatics Conference (EISIC), pages 239 -246 Aug 2013.
  8. Antounie Boutet et al. What's in your Tweet?:I know who you supported in uk 2010 General Elections. In IEEE European Intelligence and Security Informatics Conference (EISIC), 2012.
  9. M Karamibekr and Ghorbani A.Ali. Verb Oriented Sentiment Classification. In IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), pages 327 - 331, May 2012.
  10. Efthymios Kouloumpis,Theresa Wilson,Johanna Moore. Twitter Sentiment Analysis: The Good the Bad and the OMG,Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, 2011
  11. Ashish Shukla, Rahul Mishra. Sentiment Classification and Analysis Using Modifed K-Means and Nave Bayes Algorithm, International Journal of Advanced Research in Computer Science and Software Engineering , 2015.
  12. Alessia DAndrea,Fernando Ferri,Patrizia Grifoni ,Tiziana Guzzo Approaches, Tools andApplications for Sentiment Analysis Implementation ,International Journal of ComputerApplications (0975 8887) Volume 125 No.3, September , 2015.
  13. Bo Pang and Lillian Lee .Opinion mining and sentiment analysis , Foundations and Trends in Information Retrieval , 2008.
  14. M. Gjoka, M. Sirivianos, A. Markopoulou, and X. Yang.Poking facebook: characterization of osn applications." In Proceedings of the first workshop on Online social networks, WOSN,2008.
  15. Zohreh Madhoushi,Abdul Razak Hamdan,Suhaila Zainudin. Sentiment Analysis Techniques in Recent Works,In Proceedings of the first workshop on Online social networks, 2015.
  16. Amna Asmi,Tanko Ishaya. Negation Identifcation and Calculation in Sentiment Analysis, The Second International Conference on Advances in Information Mining and Management, 2012.
  17. Yahya Eru Cakra, Bayu Distiawan Trisedya. Stock Price Prediction using Linear Regression based on Sentiment Analysis , ICACSIS , 2015