ArticleExpert systems with applications2023
Social media-based COVID-19 sentiment classification model using Bi-LSTM.
Article in Expert systems with applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The analysis of bidirectional long short-term memory network model for construction of cultural gene map and information extraction.Scientific reports · 2025Article
- A Language model-based approach to sentiment classification of languages in central Asia.Scientific reports · 2025Article
- Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model.PloS one · 2025Article
- Will Public Health Emergencies Affect Compensatory Consumption Behavior? Evidence from Emotional Eating Perspective.Foods (Basel, Switzerland) · 2024Article
- The Implications of Artificial Intelligence on Infection Prevention and Control: Current Progress and Future Perspectives.China CDC weekly · 2024Article
- Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society.PloS one · 2024Article
- Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.PloS one · 2024Article
- Topic prediction for tobacco control based on COP9 tweets using machine learning techniques.PloS one · 2024Article
- Sentiment analysis of COVID-19 cases in Greece using Twitter data.Expert systems with applications · 2023Article
- From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes.Technology in society · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.