# Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweets

arXiv: https://arxiv.org/abs/2312.01178 · HTML: https://mlsanity.com/paper/2312.01178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 66 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [A Comparison of LSTM and BERT for Small Corpus](https://mlsanity.com/paper/2009.05451/) (2020)
- [Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines](https://mlsanity.com/paper/1803.07427/) (2018)
- [Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach](https://mlsanity.com/paper/2004.11695/) (2020)
- [Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs](https://mlsanity.com/paper/2005.03082/) (2020)

## Abstract (arXiv)

COVID-19 has created a major public health problem worldwide and other problems such as economic crisis, unemployment, mental distress, etc. The pandemic is deadly in the world and involves many people not only with infection but also with problems, stress, wonder, fear, resentment, and hatred. Twitter is a highly influential social media platform and a significant source of health-related information, news, opinion and public sentiment where information is shared by both citizens and government sources. Therefore an effective analysis of COVID-19 tweets is essential for policymakers to make wise decisions. However, it is challenging to identify interesting and useful content from major streams of text to understand people's feelings about the important topics of the COVID-19 tweets. In this paper, we propose a new textitframework for analyzing topic-based sentiments by extracting key topics with significant labels and classifying positive, negative, or neutral tweets on each topic to quickly find common topics of public opinion and COVID-19-related attitudes. While building our model, we take into account hybridization of BiLSTM and GRU structures for sentiment analysis to achieve our goal. The experimental results show that our topic identification method extracts better topic labels and the sentiment analysis approach using our proposed hybrid deep learning model achieves the highest accuracy compared to traditional models.