Arbane Mohamed, Benlamri Rachid, Brik Youcef, Alahmar Ayman Diyab
LASS Laboratory, Mohamed Boudiaf University, M'sila, 28000, Algeria.
University of Doha for Science and Technology, Doha, PO Box 24449, Qatar.
Expert Syst Appl. 2023 Feb;212:118710. doi: 10.1016/j.eswa.2022.118710. Epub 2022 Aug 30.
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.
互联网公共社交媒体和论坛为关注诸如新冠疫情等公共卫生问题的人们提供了一个便捷的渠道，以便他们相互分享和讨论信息/错误信息。在本文中，我们提出了一种基于双向长短期记忆（Bi-LSTM）技术的自然语言处理（NLP）方法，用于进行情感分类并揭示与新冠疫情公众舆论相关的各种问题。Bi-LSTM是传统LSTM的改进版本，用于在每个时间步从左右上下文生成输出。我们对从推特和Reddit社交媒体平台提取的真实数据集进行了实验，实验结果表明，与传统LSTM模型以及文献中最近的研究相比，我们的指标有所改善。官方机构可以使用所提出的模型来减轻负面信息的影响，并了解疫情期间人们的担忧。此外，我们的研究结果揭示了使用NLP技术分析公众舆论、打击错误信息传播以及指导健康决策的重要性。
