# COVID-19 sentiment analysis via deep learning during the rise of novel cases

arXiv: https://arxiv.org/abs/2104.10662 · HTML: https://mlsanity.com/paper/2104.10662/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 109 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Learning Gender-Neutral Word Embeddings](https://mlsanity.com/paper/1809.01496/) (2018)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Diachronic word embeddings and semantic shifts: a survey](https://mlsanity.com/paper/1806.03537/) (2018)
- [A Comparison of Word Embeddings for the Biomedical Natural Language Processing](https://mlsanity.com/paper/1802.00400/) (2018)
- [SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic](https://mlsanity.com/paper/2006.10842/) (2020)
- [Langevin-gradient parallel tempering for Bayesian neural learning](https://mlsanity.com/paper/1811.04343/) (2018)
- [Deep learning via LSTM models for COVID-19 infection forecasting in India](https://mlsanity.com/paper/2101.11881/) (2021)
- [Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic](https://mlsanity.com/paper/2008.12172/) (2020)
- [Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from A State in Australia](https://mlsanity.com/paper/2006.12185/) (2020)
- [Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown](https://mlsanity.com/paper/2102.10551/) (2021)

## Built on by (most cited first, 18 of 18)

- [Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams](https://mlsanity.com/paper/2304.10740/) (2023)
- [Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework](https://mlsanity.com/paper/2201.03115/) (2022)
- [Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita](https://mlsanity.com/paper/2205.11020/) (2022)
- [An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis](https://mlsanity.com/paper/2303.07201/) (2023)
- [Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron](https://mlsanity.com/paper/2303.00135/) (2023)
- [Large language models for newspaper sentiment analysis during COVID-19: The Guardian](https://mlsanity.com/paper/2405.13056/) (2024)
- [Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges](https://mlsanity.com/paper/2410.16204/) (2024)
- [LMR-CBT: Learning Modality-fused Representations with CB-Transformer for Multimodal Emotion Recognition from Unaligned Multimodal Sequences](https://mlsanity.com/paper/2112.01697/) (2021)
- [BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets](https://mlsanity.com/paper/2211.09733/) (2022)
- [Recursive deep learning framework for forecasting the decadal world economic outlook](https://mlsanity.com/paper/2301.10874/) (2023)
- [A clustering and graph deep learning-based framework for COVID-19 drug repurposing](https://mlsanity.com/paper/2306.13995/) (2023)
- [A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models](https://mlsanity.com/paper/2408.16942/) (2024)
- [HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models](https://mlsanity.com/paper/2501.05482/) (2025)
- [Longitudinal Abuse and Sentiment Analysis of Hollywood Movie Dialogues using Language Models](https://mlsanity.com/paper/2501.13948/) (2025)
- [An evaluation of LLMs for political bias in Western media: Israel-Hamas and Ukraine-Russia wars](https://mlsanity.com/paper/2601.06132/) (2026)
- [Large language model for Bible sentiment analysis: Sermon on the Mount](https://mlsanity.com/paper/2401.00689/) (2024)
- [Detoxify: A framework for abusive text transformation using LLMs](https://mlsanity.com/paper/2507.10177/) (2025)
- [Abusive music and song transformation using GenAI and LLMs](https://mlsanity.com/paper/2601.15348/) (2026)

## Abstract (arXiv)

Social scientists and psychologists take interest in understanding how people express emotions and sentiments when dealing with catastrophic events such as natural disasters, political unrest, and terrorism. The COVID-19 pandemic is a catastrophic event that has raised a number of psychological issues such as depression given abrupt social changes and lack of employment. Advancements of deep learning-based language models have been promising for sentiment analysis with data from social networks such as Twitter. Given the situation with COVID-19 pandemic, different countries had different peaks where the rise and fall of new cases affected lock-downs which directly affected the economy and employment. During the rise of COVID-19 cases with stricter lock-downs, people have been expressing their sentiments in social media. This can provide a deep understanding of human psychology during catastrophic events. In this paper, we present a framework that employs deep learning-based language models via long short-term memory (LSTM) recurrent neural networks for sentiment analysis during the rise of novel COVID-19 cases in India. The framework features LSTM language model with a global vector embedding and state-of-art BERT language model. We review the sentiments expressed for selective months in 2020 which covers the first major peak of novel cases in India. Our framework utilises multi-label sentiment classification where more than one sentiment can be expressed at once. Our results indicate that the majority of the tweets have been positive with high levels of optimism during the rise of the novel COVID-19 cases and the number of tweets significantly lowered towards the peak. The predictions generally indicate that although the majority have been optimistic, a significant group of population has been annoyed towards the way the pandemic was handled by the authorities.