Categorization of Event Clusters from Twitter Using Term Weighting Schemes

Surender Singh Samant, NL Bhanu Murthy, Aruna Malapati


A real-world event is commonly represented on Twitter as a collection of repetitive and noisy text messages posted by different users. Term weighting is a popular pre-processing step for text classification, especially when the size of the dataset is limited. In this paper, we propose a new term weighting scheme and a modification to an existing one and compare them with many state-of-the-art methods using three popular classifiers. We create a labelled Twitter dataset of events for exhaustive cross-validation experiments and use another Twitter event dataset for cross-corpus tests. The proposed schemes are among the best performers in many experiments, with the proposed modification significantly improving the performance of the original scheme. We create two majority voting based classifiers that further enhance the F1-scores of the best individual schemes.

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