# SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2006.10842 · HTML: https://mlsanity.com/paper/2006.10842/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 16 of 16)

- [COVID-19 sentiment analysis via deep learning during the rise of novel cases](https://mlsanity.com/paper/2104.10662/) (2021)
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- [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)
- [An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification](https://mlsanity.com/paper/2409.03203/) (2024)
- [Large language models for newspaper sentiment analysis during COVID-19: The Guardian](https://mlsanity.com/paper/2405.13056/) (2024)
- [ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination](https://mlsanity.com/paper/2201.06496/) (2022)
- [Evaluation of Google Translate for Mandarin Chinese translation using sentiment and semantic analysis](https://mlsanity.com/paper/2409.04964/) (2024)
- [An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses](https://mlsanity.com/paper/2503.21393/) (2025)
- [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)
- [Language models for longitudinal analysis of abusive content in Billboard Music Charts](https://mlsanity.com/paper/2510.06266/) (2025)
- [Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST](https://mlsanity.com/paper/2109.14456/) (2021)
- [Large language model for Bible sentiment analysis: Sermon on the Mount](https://mlsanity.com/paper/2401.00689/) (2024)
- [Automated evaluation of LLMs for effective machine translation of Mandarin Chinese to English](https://mlsanity.com/paper/2603.09998/) (2026)

## Abstract (arXiv)

Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories. As of June 18, 2020, in total, there are now over 8,400,000 cases and over 450,000 related deaths. This causes massive losses in the economy and jobs globally and confining about 58% of the global population. In this paper, we introduce SenWave, a novel sentimental analysis work using 105+ million collected tweets and Weibo messages to evaluate the global rise and falls of sentiments during the COVID-19 pandemic. To make a fine-grained analysis on the feeling when we face this global health crisis, we annotate 10K tweets in English and 10K tweets in Arabic in 10 categories, including optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, official report, and joking. We then utilize an integrated transformer framework, called simpletransformer, to conduct multi-label sentimental classification by fine-tuning the pre-trained language model on the labeled data. Meanwhile, in order for a more complete analysis, we also translate the annotated English tweets into different languages (Spanish, Italian, and French) to generated training data for building sentiment analysis models for these languages. SenWave thus reveals the sentiment of global conversation in six different languages on COVID-19 (covering English, Spanish, French, Italian, Arabic and Chinese), followed the spread of the epidemic. The conversation showed a remarkably similar pattern of rapid rise and slow decline over time across all nations, as well as on special topics like the herd immunity strategies, to which the global conversation reacts strongly negatively. Overall, SenWave shows that optimistic and positive sentiments increased over time, foretelling a desire to seek, together, a reset for an improved COVID-19 world.