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The advancement of social media contributes to the growing amount of contentthey share frequently. This framework provides a sophisticated place for peopleto report various real-life events. Detecting these events with the help ofnatural language processing has received researchers' attention, and variousalgorithms have been developed for this goal. In this paper, we propose aSemantic Modular Model (SMM) consisting of 5 different modules, namelyDistributional Denoising Autoencoder, Incremental Clustering, SemanticDenoising, Defragmentation, and Ranking and Processing. The proposed model aimsto (1) cluster various documents and ignore the documents that might notcontribute to the identification of events, (2) identify more important anddescriptive keywords. Compared to the state-of-the-art methods, the resultsshow that the proposed model has a higher performance in identifying eventswith lower ranks and extracting keywords for more important events in threeEnglish Twitter datasets: FACup, SuperTuesday, and USElection. The proposedmethod outperformed the best reported results in the mean keyword-precisionmetric by 7.9\%.

A Semantic Modular Framework for Events Topic Modeling in Social Media