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

https://doi.org/10.1016/j.eswa.2022.118710Get rights and content

Highlights

  • Bi-LSTM sentiment detection model is developed to classify COVID-19 social comments.

  • Various scenarios to help counter negative COVID-19 comments and reduce their impact.

  • Comparative analysis of our approach with most popular models is presented.

  • Word embedding followed by Bi-LSTM provides better performance on Sentiment Analysis.

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

Data availability

No data was used for the research described in the article.

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