# Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages

arXiv: https://arxiv.org/abs/2501.12540 · HTML: https://mlsanity.com/paper/2501.12540/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)

## Built on by (most cited first, 3 of 3)

- [Multilingual Sentiment Analysis of Summarized Texts: A Cross-Language Study of Text Shortening Effects](https://mlsanity.com/paper/2504.00265/) (2025)
- [GS_DravidianLangTech@2025: Women Targeted Abusive Texts Detection on Social Media](https://mlsanity.com/paper/2504.02863/) (2025)
- [Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2506.06929/) (2025)

## Abstract (arXiv)

This study explores transformer-based models such as BERT, mBERT, and XLM-R for multi-lingual sentiment analysis across diverse linguistic structures. Key contributions include the identification of XLM-R superior adaptability in morphologically complex languages, achieving accuracy levels above 88%. The work highlights fine-tuning strategies and emphasizes their significance for improving sentiment classification in underrepresented languages.