# SenWave: A Fine-Grained Multi-Language Sentiment Analysis Dataset Sourced from COVID-19 Tweets

arXiv: https://arxiv.org/abs/2510.08214 · HTML: https://mlsanity.com/paper/2510.08214/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems](https://mlsanity.com/paper/1805.04508/) (2018)
- [Sentiment Analysis of Twitter Data: A Survey of Techniques](https://mlsanity.com/paper/1601.06971/) (2016)
- [Measuring Emotions in the COVID-19 Real World Worry Dataset](https://mlsanity.com/paper/2004.04225/) (2020)
- [Mood of India During Covid-19 -- An Interactive Web Portal Based on Emotion Analysis of Twitter Data](https://mlsanity.com/paper/2005.02955/) (2020)
- [In the Eyes of the Beholder: Analyzing Social Media Use of Neutral and Controversial Terms for COVID-19](https://mlsanity.com/paper/2004.10225/) (2020)
- [BillionCOV: An Enriched Billion-scale Collection of COVID-19 tweets for Efficient Hydration](https://mlsanity.com/paper/2301.11284/) (2023)
- [CoronaVis: A Real-time COVID-19 Tweets Data Analyzer and Data Repository](https://mlsanity.com/paper/2004.13932/) (2020)
- [Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter](https://mlsanity.com/paper/2005.08817/) (2020)

## Built on by (most cited first, 1 of 1)

- [CrisisKD: Five-Stage Knowledge Distillation for Aspect-Level Sentiment and Emotion Analysis in Crisis Discourse](https://mlsanity.com/paper/2609.05757/) (2026)

## Abstract (arXiv)

The global impact of the COVID-19 pandemic has highlighted the need for a comprehensive understanding of public sentiment and reactions. Despite the availability of numerous public datasets on COVID-19, some reaching volumes of up to 100 billion data points, challenges persist regarding the availability of labeled data and the presence of coarse-grained or inappropriate sentiment labels. In this paper, we introduce SenWave, a novel fine-grained multi-language sentiment analysis dataset specifically designed for analyzing COVID-19 tweets, featuring ten sentiment categories across five languages. The dataset comprises 10,000 annotated tweets each in English and Arabic, along with 30,000 translated tweets in Spanish, French, and Italian, derived from English tweets. Additionally, it includes over 105 million unlabeled tweets collected during various COVID-19 waves. To enable accurate fine-grained sentiment classification, we fine-tuned pre-trained transformer-based language models using the labeled tweets. Our study provides an in-depth analysis of the evolving emotional landscape across languages, countries, and topics, revealing significant insights over time. Furthermore, we assess the compatibility of our dataset with ChatGPT, demonstrating its robustness and versatility in various applications. Our dataset and accompanying code are publicly accessible on the repositoryfootnotehttps://github.com/gitdevqiang/SenWave. We anticipate that this work will foster further exploration into fine-grained sentiment analysis for complex events within the NLP community, promoting more nuanced understanding and research innovations.