Abstract
With the rapid development of microblog, millions of Internet users share their opinions on different aspects of daily life. By analyzing and monitoring sentiment information extracting from tweets related to an important event, we are able to gain insights into variation trends of users’ sentiment. In this paper, we focus on extracting public sentiment of microblog emergencies. A subtopic-level opinion mining method is proposed based on two-phase optimization. Different subtopics of emergencies are extracted based on retweets. Opinion tweets are classified to different subtopics. The sentiment score of opinion holders is calculated. The above results are optimized based on users and endorsement interactions between users. Experimental results validate the effectiveness of the proposed method.
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References
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM Press, New York (2004)
Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: 42nd Annual Meeting of the Association for Computational Linguistics, pp. 271–278 (2004)
Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: 23rd International Conference on Computational Linguistics, pp. 241–249 (2010)
Silva, I.S., Gomide, J., Veloso, A., Meria, Jr., W., Ferreira, R.: Effective sentiment stream analysis with self-augmenting training and demand-driven projection. In: 34th Annual ACM SIGIR Conference, pp. 475–484 (2011)
OConnor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: 24th International AAAI Conference on Weblogs and Social Media, pp. 122–129 (2010)
Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: 23rd International Conference on Computational Linguistics: Posters, pp. 3644 (2010)
Guerra, P.H.C., Veloso, A., Meira, Jr., W., Almeida, V.: From bias to opinion: a transfer-learning approach to real-time sentiment analysis. In: 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 150–158 (2011)
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© 2015 Springer International Publishing Switzerland
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Wen, K. et al. (2015). Subtopic-Level Sentiment Analysis of Emergencies. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_28
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DOI: https://doi.org/10.1007/978-3-319-25159-2_28
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-25159-2
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