ComStreamClust: A communicative text clustering approach to topic detection in streaming data
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Topic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help governments and healthcare companies deal with this phenomenon. In this paper, we propose a novel communicative clustering approach, so-called ComStreamClust for clustering sub-topics inside a broader topic, e.g. COVID-19. The proposed approach was evaluated on two datasets: the COVID-19 and the FA CUP. The results obtained from ComStreamClust approve the effectiveness of the proposed approach when compared to existing methods such as LDA.
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