Abstract
We enhance the accuracy of the currently available semantic hashtag clustering method, which leverages hashtag semantics extracted from dictionaries such as Wordnet and Wikipedia. While immune to the uncontrolled and often sparse usage of hashtags, the current method distinguishes hashtag semantics only at the word-level. Unfortunately, a word can have multiple senses representing the exact semantics of a word, and, therefore, word-level semantic clustering fails to disambiguate the true sense-level semantics of hashtags and, as a result, may generate incorrect clusters. This paper shows how this problem can be overcome through sense-level clustering and demonstrates its impacts on clustering behavior and accuracy.
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Bhulai, S., et al.: Trend visualization on twitter: what’s hot and what’s not? In: 1st International Conference on Data Analytics, pp. 43–48 (2012)
Costa, J., Silva, C., Antunes, M., Ribeiro, B.: Defining semantic meta-hashtags for Twitter classification. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds.) ICANNGA 2013. LNCS, vol. 7824, pp. 226–235. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37213-1_24
Javed, A., Lee, B.S.: Sense-level semantic clustering of hashtags in social media. In: the 3rd Annual International Symposium on Information Management and Big Data, September 2016
Kelly, R.: Twitter study reveals interesting results about usage - 40% is pointless babble. http://pearanalytics.com/blog/2009/twitter-study-reveals-interestingresults-40-percent-pointless-babble/, Accessed 05 Oct 2016
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008). Chap. 17
Muntean, C.I., Morar, G.A., Moldovan, D.: Exploring the meaning behind Twitter hashtags through clustering. In: Abramowicz, W., Domingue, J., Węcel, K. (eds.) BIS 2012. LNBIP, pp. 231–242. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34228-8_22
Park, S., Shin, H.: Identification of implicit topics in twitter data not containing explicit search queries. In: 25th International Conference on Computational Linguistics, pp. 58–68 (2014)
Rosa, K.D., Shah, R., Lin, B.: Topical clustering of tweets. In: 3rd Workshop on Social Web Search and Mining. pp. 133–138, July 2011
Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of Twitter. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 508–524. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35176-1_32
Stilo, G., Velardi, P.: Temporal semantics: time-varying hashtag sense clustering. In: 19th International Conference on Knowledge Engineering and Knowledge Management, pp. 563–578, November 2014
Teu, P., Kraxberger, S.: Extracting semantic knowledge from Twitter. In: 3rd IFIP WG 8.5 International Conference on Electronic Participation, pp. 48–59 (2011)
Tsur, O., Littman, A., Rappoport, A.: Efficient clustering of short messages into general domains. In: 7th International AAAI Conference on Weblogs and Social Media (2013)
Tsur, O., Littman, A., Rappoport, A.: Scalable multi stage clustering of tagged micro-messages. In: International Conference on World Wide Web. pp. 621–622, April 2012
Usage Statistics. http://www.internetlivestats.com/twitter-statistics/
Vicient, C.: Moving towards the semantic web: enabling new technologies through the semantic annotation of social contents. PhD thesis. Universitat Robira I Virgili, December 2014
Vicient, C., Moreno, A.: Unsupervised semantic clustering of twitter hashtags. In: 21st European Conference on Artificial Intelligence, pp. 1119–1120, August 2014
Wang, X., et al.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: 20th ACM Conference on Information and Knowledge Management, pp. 1031–1040 (2011)
Zhibiao, W., Palmer, M.: Verbs semantics and lexical selection. In: 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138 (1994)
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Javed, A., Lee, B.S. (2017). Sense-Level Semantic Clustering of Hashtags. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig SIMBig 2015 2016. Communications in Computer and Information Science, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-319-55209-5_1
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DOI: https://doi.org/10.1007/978-3-319-55209-5_1
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