# Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

arXiv: https://arxiv.org/abs/2004.11695 · HTML: https://mlsanity.com/paper/2004.11695/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 20 of 20)

- [LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis](https://mlsanity.com/paper/2310.15100/) (2023)
- [HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding](https://mlsanity.com/paper/2210.10625/) (2022)
- [From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews](https://mlsanity.com/paper/2312.01202/) (2023)
- [BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets](https://mlsanity.com/paper/2211.09733/) (2022)
- [Facilitating Opinion Diversity through Hybrid NLP Approaches](https://mlsanity.com/paper/2405.09439/) (2024)
- [The Evolution of Rumors on a Closed Platform during COVID-19](https://mlsanity.com/paper/2104.13816/) (2021)
- [Surveillance of COVID-19 Pandemic using Social Media: A Reddit Study in North Carolina](https://mlsanity.com/paper/2106.04515/) (2021)
- [Sentiment Analysis of the COVID-related r/Depression Posts](https://mlsanity.com/paper/2108.06215/) (2021)
- [A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets](https://mlsanity.com/paper/2111.08658/) (2021)
- [Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data](https://mlsanity.com/paper/2112.04351/) (2021)
- [Exploring COVID-19 Related Stressors Using Topic Modeling](https://mlsanity.com/paper/2202.00476/) (2022)
- [COVID-19-related Nepali Tweets Classification in a Low Resource Setting](https://mlsanity.com/paper/2210.05425/) (2022)
- [Recursive deep learning framework for forecasting the decadal world economic outlook](https://mlsanity.com/paper/2301.10874/) (2023)
- [Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies](https://mlsanity.com/paper/2410.13247/) (2024)
- [Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation](https://mlsanity.com/paper/2101.06484/) (2021)
- [Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic](https://mlsanity.com/paper/2105.08165/) (2021)
- [Topic Modeling and Progression of American Digital News Media During the Onset of the COVID-19 Pandemic](https://mlsanity.com/paper/2106.09572/) (2021)
- [Forecasting User Interests Through Topic Tag Predictions in Online Health Communities](https://mlsanity.com/paper/2211.02789/) (2022)
- [Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweets](https://mlsanity.com/paper/2312.01178/) (2023)
- [COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions](https://mlsanity.com/paper/2402.09897/) (2024)

## Abstract (arXiv)

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.