  The advancement of social media contributes to the growing amount of content
they share frequently. This framework provides a sophisticated place for people
to report various real-life events. Detecting these events with the help of
natural language processing has received researchers' attention, and various
algorithms have been developed for this goal. In this paper, we propose a
Semantic Modular Model (SMM) consisting of 5 different modules, namely
Distributional Denoising Autoencoder, Incremental Clustering, Semantic
Denoising, Defragmentation, and Ranking and Processing. The proposed model aims
to (1) cluster various documents and ignore the documents that might not
contribute to the identification of events, (2) identify more important and
descriptive keywords. Compared to the state-of-the-art methods, the results
show that the proposed model has a higher performance in identifying events
with lower ranks and extracting keywords for more important events in three
English Twitter datasets: FACup, SuperTuesday, and USElection. The proposed
method outperformed the best reported results in the mean keyword-precision
metric by 7.9\%.
