Social media-based COVID-19 sentiment classification model using Bi-LSTM

Mohamed Arbane; Rachid Benlamri; Youcef Brik; Ayman Diyab Alahmar

doi:10.1016/j.eswa.2022.118710

Social media-based COVID-19 sentiment classification model using Bi-LSTM

Expert Syst Appl. 2023 Feb:212:118710. doi: 10.1016/j.eswa.2022.118710. Epub 2022 Aug 30.

Authors

Mohamed Arbane¹, Rachid Benlamri², Youcef Brik¹, Ayman Diyab Alahmar³

Affiliations

¹ LASS Laboratory, Mohamed Boudiaf University, M'sila, 28000, Algeria.
² University of Doha for Science and Technology, Doha, PO Box 24449, Qatar.
³ Department of Software Engineering, Lakehead University, Thunder Bay, P7B 5E1, Ontario, Canada.

Abstract

Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples' concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making.

Keywords: Bi-LSTM; COVID-19; Deep learning; Natural language processing; Sentiment classification; Social media.