Open Access Open Access  Restricted Access Subscription or Fee Access

Opinions mining of Twitter events using spatial-temporal features

Mudasir Mohd, Dr Rana Hashmy

Abstract


Micro-blogs are a powerful tool to express an opinion. Twitter is one of the fastest growing micro-blog and has more than 900 million users. Twitter is a rich source of opinion as users share their daily experience of life and respond to specific events using tweets on twitter. In this paper, an automatic opinion classifier capable of automatically classify tweets into different opinion expressed by them is developed. Also manually annotated corpus for opining mining to be used by supervised learning algorithms is designed. Opinion classifier uses semantic, lexical, domain dependent and context features for classification. Results obtained confirm competitive performance and robustness of the system. Classifier accuracy is more than 75%, which is higher than the baseline accuracy.


Keywords


Opinion Mining; Supervised learning; Twitter events; twitter corpus

Full Text:

PDF

References


Java, A., Song, X., Finin, T. and Tseng, B., 2007, August. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (pp. 56-65). ACM.

Turney, Peter D. "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews." Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2002.

Turney, Peter D., and Michael L. Littman. "Measuring praise and criticism: Inference of semantic orientation from association." ACM Transactions on Information Systems (TOIS) 21.4 (2003): 315-346.

Dave, Kushal, Steve Lawrence, and David M. Pennock. "Mining the peanut gallery: Opinion extraction and semantic classification of product reviews." Proceedings of the 12th international conference on World Wide Web. ACM, 2003.

Das, Sanjiv, and Mike Chen. "Yahoo! for Amazon: Extracting market sentiment from stock message boards." Proceedings of the Asia Pacific finance association annual conference (APFA). Vol. 35. 2001.

Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. "Thumbs up?: sentiment classification using machine learning techniques." Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 2002.

Pang, Bo, and Lillian Lee. "A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts." Proceedings of the 42nd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2004.

Zhuang, Li, Feng Jing, and Xiao-Yan Zhu. "Movie review mining and summarization." Proceedings of the 15th ACM international conference on Information and knowledge management. ACM, 2006.

Popescu, Ana-Maria, Bao Nguyen, and Oren Etzioni. "OPINE: Extracting product features and opinions from reviews." Proceedings of HLT/EMNLP on interactive demonstrations. Association for Computational Linguistics, 2005.

Kouloumpis, Efthymios, Theresa Wilson, and Johanna D. Moore. "Twitter sentiment analysis: The good the bad and the omg!." Icwsm 11.538-541 (2011): 164.

Saif, Hassan, Yulan He, and Harith Alani. "Semantic sentiment analysis of twitter." International semantic web conference. Springer, Berlin, Heidelberg, 2012.

Sarlan, Aliza, Chayanit Nadam, and Shuib Basri. "Twitter sentiment analysis." Information Technology and Multimedia (ICIMU), 2014 International Conference on. IEEE, 2014.

Balabantaray, Rakesh C., Mudasir Mohammad, and Nibha Sharma. "Multi-class twitter emotion classification: A new approach." International Journal of Applied Information Systems 4.1 (2012): 48-53.

Canales, Lea, et al. "Exploiting a bootstrapping approach for automatic annotation of emotions in texts." Data Science and Advanced Analytics (DSAA), 2016 IEEE International Conference on. IEEE, 2016.

Mikolov, Tomas, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013).

Hall, Mark, et al. "The WEKA data mining software: an update." ACM SIGKDD explorations newsletter11.1 (2009): 10-18.

Joachims, Thorsten, Thomas Finley, and Chun-Nam John Yu. "Cutting-plane training of structural SVMs." Machine Learning 77.1 (2009): 27-59.

Manning, Christopher, et al. "The Stanford CoreNLP natural language processing toolkit." Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. 2014.

Miller, George A. "WordNet: a lexical database for English." Communications of the ACM 38.11 (1995): 39-41.

Van Rijsbergen, Cornelis J., Stephen Edward Robertson, and Martin F. Porter. New models in probabilistic information retrieval. London: British Library Research and Development Department, 1980.

Mikolov, Tomas, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013).

K. Crammer and Y. Singer. On the Algorithmic Implementation of Multi-class SVMs, JMLR, 2001


Refbacks

  • There are currently no refbacks.