Abstract
Analysis of tweets would help in designing smart recommendation systems. Analysis of twitter messages is an interesting research area. Sentiment analysis of tweets has been done in some works. Another line of work is the classification of tweets into different categories. However, there are few works that have considered both sentiment analysis and classification to find out users’ interest. In this paper, we propose an approach that combines both sentiment analysis and classification. Thus we are able to extract the topic in which users are interested. We have implemented our algorithm using five lakhs of tweets and around one thousand of users. The results are quite encouraging.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Prabowo, R., Thelwall, M.: Sentiment analysis: a combined approach. J. Informetrics 3(2), 143–157 (2009)
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment Treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)
Thelwall, M.: Heart and soul: sentiment strength detection in the social web with SentiStrength. In: Proceedings of the CyberEmotions, pp. 1–14 (2013)
Gurini, D.F., Gasparetti, F., Micarelli, A., Sansonetti, G.: A sentiment-based approach to Twitter user recommendation. In: Proceedings of 5th ACM RecSys workshop on Recommender Systems and the Social Web, June 2013
Agarwal, V., Bharadwaj, K.K.: A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity. J. Soc. Netw. Anal. Min. 3, 359–379 (2013)
Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of Twitter posts for user profile construction on the social web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)
Boutet, A., Kim, H., Yoneki, E.: What’s in Twitter, I know what parties are popular and who you are supporting now! J. Soc. Netw. Anal. Min. 3(4), 1379–1391 (2013)
Java, A., Song, X., Finin, T., Tseng, B.: Why we Twitter: understanding microblogging usage and communities. In: Proceedings of 9th WebKDD and 1st SNA-KDD Workshop, SanJose, California, USA, pp. 56–65, August 2007
Pennacchiotti, M., Popescu, A.: A machine learning approach to Twitter user classification. In: Proceedings of the Fifth ICWSM, pp. 281–288 (2011)
Lee, K., Palsetia, D., Narayanan, R., Patwary, M.M.A., Agrawal, A., Choudhary, A.: Twitter trending topic classification. In: Proceedings of 11th IEEE International Conference on Data Mining Workshops, pp. 251–258, December 2011
Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50(1), 723–762 (2014). USA
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data. In: Proceeding of LSM 2011 Workshop on Languages in Social Media, pp. 30–38. Association for Computational Linguistics (2011)
Manning, D., Christopher, M., Surdeanu, J., Bauer, J., Finkel, S., Bethard, J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Proceedings of the 10th European Conference on Machine Learning, London, pp. 137–142 (1998)
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: what 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (2010)
Acknowledgement
The authors thank the anonymous reviewers for their valuable suggestions that have helped in improving the paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Mangal, N., Niyogi, R., Milani, A. (2016). Analysis of Users’ Interest Based on Tweets. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9790. Springer, Cham. https://doi.org/10.1007/978-3-319-42092-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-42092-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42091-2
Online ISBN: 978-3-319-42092-9
eBook Packages: Computer ScienceComputer Science (R0)