# Large language models for newspaper sentiment analysis during COVID-19: The Guardian

arXiv: https://arxiv.org/abs/2405.13056 · HTML: https://mlsanity.com/paper/2405.13056/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 94 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 18 of 18)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [Kaggle forecasting competitions: An overlooked learning opportunity](https://mlsanity.com/paper/2009.07701/) (2020)
- [Dropout improves Recurrent Neural Networks for Handwriting Recognition](https://mlsanity.com/paper/1312.4569/) (2013)
- [COVID-19 sentiment analysis via deep learning during the rise of novel cases](https://mlsanity.com/paper/2104.10662/) (2021)
- [SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic](https://mlsanity.com/paper/2006.10842/) (2020)
- [Spark NLP: Natural Language Understanding at Scale](https://mlsanity.com/paper/2101.10848/) (2021)
- [An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis](https://mlsanity.com/paper/2303.07201/) (2023)
- [An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines](https://mlsanity.com/paper/2306.13797/) (2023)
- [Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media](https://mlsanity.com/paper/2401.10841/) (2024)
- [Understanding COVID-19 News Coverage using Medical NLP](https://mlsanity.com/paper/2203.10338/) (2022)

## Built on by (most cited first, 4 of 4)

- [Evaluation of Google Translate for Mandarin Chinese translation using sentiment and semantic analysis](https://mlsanity.com/paper/2409.04964/) (2024)
- [HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models](https://mlsanity.com/paper/2501.05482/) (2025)
- [Language models for longitudinal analysis of abusive content in Billboard Music Charts](https://mlsanity.com/paper/2510.06266/) (2025)
- [Free Access to World News: Reconstructing Full-Text Articles from GDELT](https://mlsanity.com/paper/2504.16063/) (2025)

## Abstract (arXiv)

During the COVID-19 pandemic, the news media coverage encompassed a wide range of topics that includes viral transmission, allocation of medical resources, and government response measures. There have been studies on sentiment analysis of social media platforms during COVID-19 to understand the public response given the rise of cases and government strategies implemented to control the spread of the virus. Sentiment analysis can provide a better understanding of changes in societal opinions and emotional trends during the pandemic. Apart from social media, newspapers have played a vital role in the dissemination of information, including information from the government, experts, and also the public about various topics. A study of sentiment analysis of newspaper sources during COVID-19 for selected countries can give an overview of how the media covered the pandemic. In this study, we select The Guardian newspaper and provide a sentiment analysis during various stages of COVID-19 that includes initial transmission, lockdowns and vaccination. We employ novel large language models (LLMs) and refine them with expert-labelled sentiment analysis data. We also provide an analysis of sentiments experienced pre-pandemic for comparison. The results indicate that during the early pandemic stages, public sentiment prioritised urgent crisis response, later shifting focus to addressing the impact on health and the economy. In comparison with related studies about social media sentiment analyses, we found a discrepancy between The Guardian with dominance of negative sentiments (sad, annoyed, anxious and denial), suggesting that social media offers a more diversified emotional reflection. We found a grim narrative in The Guardian with overall dominance of negative sentiments, pre and during COVID-19 across news sections including Australia, UK, World News, and Opinion