Abstract
Twitter is a well-known social network service. Every second, users post a large number of tweets on different topics, which leads to a significant problem-it is time-consuming for users to get useful information for their individual purposes. It is difficult for a user to receive necessary information from all topics with high accuracy. Thus, integrating the tweets to create summaries is very convenient solution for users. There are some previous works trying to solve the problem of tweet integration. However, they did not consider automatic grouping tweets into small clusters according to topic. Moreover, the tweets have not analyzed for sentiment mining before summarization. In this study, we propose an approach to integrate tweets by taking into account techniques such as topic modeling to automatically determine the number of topics as well as the tweets inside each topic, plus sentiment analysis to classify the attitudes of the users. The experimental results show that the proposed model achieves promising results.
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References
Alvarez-Melis, D., Saveski, M.: Topic modeling in twitter: aggregating tweets by conversations. In: ICWSM, pp. 519–522 (2016)
Sharifi, B., Hutton, M.A., Kalita, J.: Summarizing microblogs automatically. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 685–688. Association for Computational Linguistics (2010)
Yajuan, D., Zhimin, C., Furu, W., Ming, Z., Shum, H.Y.: Twitter topic summarization by ranking tweets using social influence and content quality. In: Proceedings of the 24th International Conference on Computational Linguistics, pp. 763–780 (2012)
Zhang, R., Li, W., Gao, D., Ouyang, Y.: Automatic twitter topic summarization with speech acts. IEEE Trans. Audi Speech Lang. Process. 21(3), 649–658 (2013)
Sharifi, B., Hutton, M.A., Kalita, J.K.: Experiments in microblog summarization. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 49–56. IEEE (2010)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)
Zhou, S., Li, K., Liu, Y.: Text categorization based on topic model. Int. J. Comput. Intell. Syst. 2(4), 398–409 (2009). https://doi.org/10.1080/18756891.2009.9727671
Heinrich, G.: Infinite LDA implementing the HDP with minimum code complexity. Technical note, Feb 170 (2011)
Hoang, D.T., Tran, V.C., Nguyen, V.D., Nguyen, N.T., Hwang, D.: Improving academic event recommendation using research similarity and interaction strength between authors. Cybern. Syst. 48(3), 210–230 (2017)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning (ICM-14), pp. 1188–1196 (2014)
Filippova, K.: Multi-sentence compression: finding shortest paths in word graphs. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 322–330. Association for Computational Linguistics (2010)
Steinberger, J., Ježek, K.: Evaluation measures for text summarization. Comput. Inf. 28(2), 251–275 (2012)
Vanderwende, L., Suzuki, H., Brockett, C., Nenkova, A.: Beyond sumbasic: task-focused summarization with sentence simplification and lexical expansion. Inf. Process. Manag. 43(6), 1606–1618 (2007)
Erkan, G., Radev, D.R.: Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)
Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B4009410).
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Phan, H.T., Hoang, D.T., Nguyen, N.T., Hwang, D. (2018). Tweet Integration by Finding the Shortest Paths on a Word Graph. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_8
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