Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

SATLabel: A Framework for Sentiment and Aspect Terms Based Automatic Topic Labeling

Version 1 : Received: 1 April 2022 / Approved: 5 April 2022 / Online: 5 April 2022 (11:58:02 CEST)

How to cite: 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. Preprints 2022, 2022040026 (doi: 10.20944/preprints202204.0026.v1). 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. Preprints 2022, 2022040026 (doi: 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.

Keywords

Data-driven Framework; LDA; Sentiment Terms; Aspect Terms; Unigrams; Soft Cosine Similarity; Topic; Automatic labeling

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.