Abstract
Twitter has become the leading platform for mining information related to real-life events. A large amount of the shared content in Twitter are non-informative spams and informal personal updates. Thus, it is necessary to identify and rank informative event-specific content from Twitter. Moreover, tweets containing information about named entities (like person, place, organization, etc.) occurring in the context of an event, generates interest and aids in gaining useful insights. In this paper, we develop a novel generic model based on the principle of mutual reinforcement, for representing and identifying event-specific, as well as entity-centric informative content from Twitter. An algorithm is proposed that ranks tweets in terms of event-specific, entity-centric information content by leveraging the semantics of relationships between different units of the model.
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Notes
- 1.
List of resources like slang words, stopwords and feeling words used can be obtained from https://github.com/dxmahata/EIIMFramework/tree/master/CodeBase/EventIdentityInformationManagement/Resources.
References
Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on twitter. In: ICWSM, vol. 11, pp. 438–441 (2011)
Olteanu, A., Castillo, C., Diaz, F., Vieweg, S.: Crisislex: a lexicon for collecting and filtering microblogged communications in crises. In: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM 2014), number EPFL-CONF-203561 (2014)
Popescu, A.-M., Pennacchiotti, M., Paranjpe, D.: Extracting events and event descriptions from twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 105–106. ACM (2011)
Sakaki, T., Okazaki, M., Matsuo, Y.: Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans. Knowl. Data Eng. 25(4), 919–931 (2013)
Shaw, R., Troncy, R., Hardman, L.: LODE: linking open descriptions of events. In: Gómez-Pérez, A., Yu, Y., Ding, Y. (eds.) ASWC 2009. LNCS, vol. 5926, pp. 153–167. Springer, Heidelberg (2009)
Wei, F., Li, W., Lu, Q., He, Y.: Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 283–290. ACM (2008)
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Mahata, D., Talburt, J.R., Singh, V.K. (2015). Identification and Ranking of Event-Specific Entity-Centric Informative Content from Twitter. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_24
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DOI: https://doi.org/10.1007/978-3-319-19581-0_24
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