# M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets

arXiv: https://arxiv.org/abs/2404.01753 · HTML: https://mlsanity.com/paper/2404.01753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [SemEval-2013 Task 2: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.06806/) (2019)
- [BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs](https://mlsanity.com/paper/1704.06125/) (2017)
- [Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination](https://mlsanity.com/paper/1507.00955/) (2015)
- [A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts](https://mlsanity.com/paper/2205.04185/) (2022)
- [On the logistical difficulties and findings of Jopara Sentiment Analysis](https://mlsanity.com/paper/2105.02947/) (2021)
- [A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods](https://mlsanity.com/paper/2106.08829/) (2021)
- [How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies](https://mlsanity.com/paper/2104.10513/) (2021)

## Built on by (most cited first, 2 of 2)

- [Extending a Parliamentary Corpus with MPs' Tweets: Automatic Annotation and Evaluation Using MultiParTweet](https://mlsanity.com/paper/2512.11567/) (2025)
- [Model in Distress: Sentiment Analysis on French Synthetic Social Media](https://mlsanity.com/paper/2604.18226/) (2026)

## Abstract (arXiv)

In recent years, multimodal natural language processing, aimed at learning from diverse data types, has garnered significant attention. However, there needs to be more clarity when it comes to analysing multimodal tasks in multi-lingual contexts. While prior studies on sentiment analysis of tweets have predominantly focused on the English language, this paper addresses this gap by transforming an existing textual Twitter sentiment dataset into a multimodal format through a straightforward curation process. Our work opens up new avenues for sentiment-related research within the research community. Additionally, we conduct baseline experiments utilising this augmented dataset and report the findings. Notably, our evaluations reveal that when comparing unimodal and multimodal configurations, using a sentiment-tuned large language model as a text encoder performs exceptionally well.