Preferences help
enabled [disable] Abstract
Number of results
2019 | 121 | 73-82
Article title

Twitter Opinion Mining Using Sentiment Analysis

Title variants
Languages of publication
This is very important to extract verdict or opinion of others about any product, topic or about some person. The rich sources of this opinion rich data are blogs, online review sites and social networking sites. Among the social networking sites, twitter one of such largest source of microblog has gained popularity with more than 500 million tweets per day. Because of this Twitter has become a primary source for opinion mining. Twitter messages called tweets, are much focused because of the restricted characters size of 140 characters. Social network data is one of the most effective and accurate indicators of public sentiment. In this paper, twitter data is analyzed to determine the opinion of public. Twitter data about iPhone 6 is collected for analysis using the Twitter public API which allows developers to extract tweets from twitter programmatically. In this paper, Naïve Bayes classifier is used to calculate the sentiments of tweets and compared with baseline algorithms.
Physical description
  • Atal Bihari Vajpayee Indian Institute of Information Technology, Gwalior, India
  • KIET Group of Institutions, Ghaziabad, India
  • Atal Bihari Vajpayee Indian Institute of Information Technology, Gwalior, India
  • [1] K. Ravi and V. Ravi, A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems, vol. 89, pp. 14–46, 2015.
  • [2] S. Asur and B. A. Huberman, Predicting the future with social media, in Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology vol. 1. IEEE, 2010, pp. 492–499.
  • [3] K. Denecke, Using SentiWordNet for multilingual sentiment analysis, in Data Engineering Workshop, 2008. ICDEW 2008. IEEE 24th International Conference on Data Engineering Workshop, IEEE, 2008, pp. 507–512.
  • [4] F. H. Khan, S. Bashir, and U. Qamar, Tom: Twitter opinion mining framework using hybrid classification scheme, Decision Support Systems, vol. 57, pp. 245–257, 2014.
  • [5] H. Chen and D. Zimbra, Ai and opinion mining, IEEE Intelligent Systems, vol. 25, no. 3, pp. 74–80, 2010.
  • [6] M. Gamon, A. Aue, S. Corston-Oliver, and E. Ringger, Pulse: Mining customer opinions from free text, in International Symposium on Intelligent Data Analysis. Springer, 2005, pp. 121–132.
  • [7] Z. Khan, M. Atique, and V. Thakare, Combining lexicon-based and learning-based methods for twitter sentiment analysis. International Journal of Electronics, Communication and Soft Computing Science & Engineering 89, 2015.
  • [8] Balahur, R. Steinberger, E. Van Der Goot, B. Pouliquen, and M. Kabadjov, Opinion mining on newspaper quotations,‖ in Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT’09. IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, vol. 3. IEEE, 2009, pp. 523–526.
  • [9] M. Thelwall, K. Buckley, and G. Paltoglou, Sentiment in twitter events. Journal of the American Society for Information Science and Technology, vol. 62, no. 2, pp. 406–418, 2011.
  • [10] E. Cambria, B. Schuller, Y. Xia, and C. Havasi, ―New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, vol. 28, no. 2, pp. 15–21, 2013.
  • [11] Kumar and T. M. Sebastian, ―Sentiment analysis on twitter. International Journal of Computer Science Issues, vol. 9, no. 3, pp. 372–378, 2012.
  • [12] M. Gamon, A. Aue, S. Corston-Oliver, and E. Ringger, Pulse: Mining customer opinions from free text,‖ in international symposium on intelligent data analysis. Springer, 2005, pp. 121–132.
  • [13] M. Eirinaki, S. Pisal, and J. Singh, ―Feature-based opinion mining and ranking,‖ Journal of Computer and System Sciences, vol. 78, no. 4, pp. 1175–1184, 2012.
  • [14] M. M. Mostafa, More than words: Social networks text mining for consumer brand sentiments, Expert Systems with Applications, vol. 40, no. 10, pp. 4241–4251, 2013.
  • [15] E. Cambria, R. Speer, C. Havasi, and A. Hussain. Senticnet: A publicly available semantic resource for opinion AAAI fall symposium: common sense knowledge, vol. 10, 2010.
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.