Abstract
The aim of this research is to easily monitor the reputation of a company in the Twittersphere. We propose a strategy that organizes a stream of tweets into different clusters based on the tweets’ topics. Furthermore, the obtained clusters are assigned into different priority levels. A cluster with high priority represents a topic which may affect the reputation of a company, and that consequently deserves immediate attention. The evaluation results show that our method is competitive even though the method does not make use of any external knowledge resource.
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References
Amigó, E., Corujo, A., Gonzalo, J., Meij, E., de Rijke, M.: Overview of replab 2012: Evaluating online reputation management systems. In: CLEF 2012 Labs and Workshop Notebook Papers (2012)
Amigo, E., Gonzalo, J., Verdejo, F.: Reliability and Sensitivity: Generic Evaluation Measures for Document Organization Tasks. UNED, Madrid, Spain, Technical Report (2012)
Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: Real-world event identification on twitter. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, ICWSM 2011 (2011)
Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 241–249. Association for Computational Linguistics (2010)
Diao, Q., Jiang, J., Zhu, F., Lim, E.-P.: Finding bursty topics from microblogs. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 536–544. Association for Computational Linguistics (2012)
Ellen, J.: All about microtext: A working definition and a survey of current microtext research within artificial intelligence and natural language processing. In: Proceedings of the Third International Conference on Agents and Artificial Intelligence (2011)
Hong, L., Davison, B.D.: Empirical study of topic modeling in twitter. In: Proceedings of the First Workshop on Social Media Analytics, pp. 80–88. ACM (2010)
Ilina, E., Hauff, C., Celik, I., Abel, F., Houben, G.-J.: Social event detection on twitter. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds.) ICWE 2012. LNCS, vol. 7387, pp. 169–176. Springer, Heidelberg (2012)
Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: Tweets as electronic word of mouth. J. Am. Soc. Inf. Sci. Technol. 60(11), 2169–2188 (2009)
O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. In: Proceedings of the International AAAI Conference on Weblogs and Social Media, pp. 122–129 (2010)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC (2010)
Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to twitter. In: Human Language Technologies: The, Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 181–189. Association for Computational Linguistics, Stroudsburg (2010)
Ramage, D., Dumais, S., Liebling, D.: Characterizing microblogs with topic models. In: International AAAI Conference on Weblogs and Social Media, vol. 5, pp. 130–137 (2010)
Ritter, A., Clark, S., Mausam, Etzioni, O.: Named entity recognition in tweets: an experimental study. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pp. 1524–1534. Association for Computational Linguistics, Stroudsburg (2011)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 851–860. ACM, New York (2010)
Toutanova, K., Manning, C.D.: Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In: Proceedings of the 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora: Held in Conjunction with the 38th Annual Meeting of the Association for Computational Linguistics, EMNLP 2000, vol. 13, pp. 63–70. Association for Computational Linguistics, Stroudsburg (2000)
Weng, J., Lee, B.-S.: Event detection in twitter. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media, vol. 3 (2011)
Weng, J., Lim, E.-P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)
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Qureshi, M.A., O’Riordan, C., Pasi, G. (2013). Clustering with Error-Estimation for Monitoring Reputation of Companies on Twitter. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_15
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DOI: https://doi.org/10.1007/978-3-642-45068-6_15
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