Abstract
Nowadays, many twitter users tweet their personal affairs. Some of these posts can be quite beneficial for real life, for example, Eating, Appearance, Living, Disasters, and so on. In this paper, we propose a two phase extracting method for selecting beneficial tweets. In the first phase, many topics are extracted from a sea of tweets using Latent Dirichlet Allocation (LDA). In the second phase, associations between many topics and fewer aspects is built using a small set of labeled tweets. To enhance accuracy, the weight of feature words is calculated by information gain. Our prototype system demonstrates that the proposed method can extract the aspects of each unknown tweet.
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References
Yamamoto, M., Ogasawara, H., Suzuki, I., Furukawa, M.: Tourism informatics:9. information propagation network for 2012 tohoku earthquake and tsunami on twitter. IPSJ Magazine 53(11), 1184–1191 (2012) (in Japanese)
Yamamoto, S., Satoh, T.: Real life information extraction method from twitter. In: The 4th Forum on Data Engineering and Information Management (DEIM 2012) F3-4 (2012) (in Japanese)
Kurashima, T., Tezuka, T., Tanaka, K.: Blog map of experiences: Extracting and geographically mapping visitor experiences from urban blogs. In: Ngu, A.H.H., Kitsuregawa, M., Neuhold, E.J., Chung, J.-Y., Sheng, Q.Z. (eds.) WISE 2005. LNCS, vol. 3806, pp. 496–503. Springer, Heidelberg (2005)
Inui, K., Abe, S., Morita, H., Eguchi, M., Sumida, A., Sao, C., Hara, K., Murakami, K., Matsuyoshi, S.: Experience mining: Building a large-scale database of personal experiences and opinions from web documents. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 314–321 (2008)
Ramage, D., Dumais, S., Liebling, D.: Characterizing microblogs with topic models. In: Proceedings of ICWSM 2010, pp. 130–137 (2010)
Bollen, J., Pepe, A., Mao, H.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: Proceedings of WWW 2010, pp. 450–453 (2010)
Diakopoulous, N.A., Shamma, D.A.: Characterizing debate performance via aggregated twitter sentiment. In: Proceedings of CHI 2010, pp. 1195–1198 (2010)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: Real-time event detection by social sensors. In: Proceedings of 18th International World Wide Web Conference, WWW 2010, pp. 851–860 (2010)
Zhao, X., Jiang, J., He, J., Song, Y., Achananuparp, P., Lim, E.P., Li, X.: Topical key phrase extraction from twitter. In: The 49th Annual Meeting of the Association for Computational Linguistics, pp. 379–388 (2011)
Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: Proceedings of the 2010 International Conference on Management of Data, pp. 1155–1158 (2010)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Science 101, 5228–5235 (2004)
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Yamamoto, S., Satoh, T. (2013). Two Phase Extraction Method for Extracting Real Life Tweets Using LDA. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_35
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DOI: https://doi.org/10.1007/978-3-642-37401-2_35
Publisher Name: Springer, Berlin, Heidelberg
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