Shahriar, K.T.; Moni, M.A.; Hoque, M.M.; Islam, M.N.; Sarker, I.H. SATLabel: A Framework for Sentiment and Aspect Terms Based Automatic Topic Labeling. Preprints2022, 2022040026. https://doi.org/10.20944/preprints202204.0026.v1
APA Style
Shahriar, K.T., Moni, M.A., Hoque, M.M., Islam, M.N., & Sarker, I.H. (2022). SATLabel: A Framework for Sentiment and Aspect Terms Based Automatic Topic Labeling. Preprints. https://doi.org/10.20944/preprints202204.0026.v1
Chicago/Turabian Style
Shahriar, K.T., Muhammad Nazrul Islam and Iqbal H. Sarker. 2022 "SATLabel: A Framework for Sentiment and Aspect Terms Based Automatic Topic Labeling" Preprints. https://doi.org/10.20944/preprints202204.0026.v1
Abstract
In this paper, we present a framework that automatically labels Latent Dirichlet Allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the cognitive overhead of identifying key topics labels. Social media platforms especially Twitter are considered as one of the most influential sources of information for providing public opinion related to a critical situation like the COVID-19 pandemic. LDA is a popular topic modelling algorithm that extracts hidden themes of documents without assigning a specific label. Thus automatic labelling of LDA-generated topics from COVID-19 tweets is a great challenge instead of following the manual labelling approach to get an overview of wider public opinion. To overcome this problem, in this paper, we propose a framework named \texttt{SATLabel} that effectively identifies significant topic labels using \textit{top unigrams features of sentiment terms and aspect terms clusters from LDA generated topics} of COVID-19 related tweets to uncover various issues related to the COVID-19 pandemic. The experimental results show that our methodology is more effective, simpler, and traces better topic labels compare to the manual topic labelling approach.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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