Abstract
In this paper, we propose a novel approach for term weighting in very short documents that is used with a Support Vector Machine classifier. We focus on market research and social media documents. In both of these data sources, the average length of a document is below twenty words. As the documents are short, each word occurs usually only once within a document. This is known as hapax legomenon and in our previous work as Term Frequency=1 challenge. For this reason, the traditional term weighting approaches become less effective with short documents. In this paper we propose a novel approach for term weighting that does not use term frequency within a document but substitutes it with other word statistics. In the experimental evaluation and comparison against several other term weighting approaches the proposed method produced promising results by out-performing the competition.
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References
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24, 513–523 (1988)
Timonen, M., Silvonen, P., Kasari, M.: Classification of short documents to categorize consumer opinions. In: Online Proceedings of 7th International Conference on Advanced Data Mining and Applications (ADMA 2011), China (2011), http://aminer.org/PDF/adma2011/session3D/adma11_conf_32.pdf (accessed October 10, 2012)
Timonen, M.: Categorization of very short documents. In: Internation Conference on Knowledge Discovery and Information Retrieval (KDIR 2012), Spain, pp. 5–16 (2012)
Forman, G.: An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research 3, 1289–1305 (2003)
Rennie, J.D.M., Jaakkola, T.: Using term informativeness for named entity detection. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2005), Brazil, pp. 353–360 (2005)
Clark, K., Gale, W.: Inverse Document Frequency (IDF): A measure of deviation from Poisson. In: Third Workshop on Very Large Corpora, pp. 121–130. Massachusetts Institute of Technology, Cambridge (1995)
Mladenic, D., Grobelnik, M.: Feature selection for unbalanced class distribution and Naive Bayes. In: Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Slovenia, pp. 258–267 (1999)
Forman, G.: BNS feature scaling: an improved representation over TF-IDF for SVM text classification. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM 2008), USA, pp. 263–270 (2008)
Yang, Y., Pedersen, J.: Feature selection in statistical learning of text categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), USA, pp. 412–420 (1997)
Yang, Y.: An evaluation of statistical approaches to text categorization. Information Retrieval 1, 69–90 (1999)
Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1999), USA, pp. 42–49 (1999)
Krishnakumar, A.: Text categorization building a kNN classifier for the Reuters-21578 collection (2006), http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.9946 (accessed October 10, 2012)
Joachims, T.: Text categorization with Support Vector Machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Rennie, J.D., Shih, L., Teevan, J., Karger, D.R.: Tackling the poor assumptions of Naive Bayes text classifiers. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), USA, pp. 616–623 (2003)
Kibriya, A.M., Frank, E., Pfahringer, B., Holmes, G.: Multinomial Naive Bayes for text categorization revisited. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 488–499. Springer, Heidelberg (2004)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2010), Malta (2010)
Irani, D., Webb, S., Pu, C., Li, K.: Study of trend-stuffing on Twitter through text classification. In: Seventh Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (CEAS 2010), USA (2010), http://ceas.cc/2010/papers/Paper%2013.pdf (accessed October 10, 2012)
Benevenuto, F., Mango, G., Rodrigues, T., Almeida, V.: Detecting spammers on Twitter. In: Seventh Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (CEAS 2010), USA (2010), http://ceas.cc/2010/papers/Paper%2021.pdf (accessed October 10, 2012)
Joachims, T.: Making large-Scale SVM Learning Practical. In: Advances in Kernel Methods - Support Vector Learning, pp. 41–56. MIT Press (1999)
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Timonen, M., Kasari, M. (2013). Statistical Approach for Term Weighting in Very Short Documents for Text Categorization. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2012. Communications in Computer and Information Science, vol 415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54105-6_1
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DOI: https://doi.org/10.1007/978-3-642-54105-6_1
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