mlsanityData to 7 Oct 2026

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

arXiv 2501.12540 ↗First posted 21 Jan 2025cs.CLcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 21 references that are in the map. First posted on arXiv 21 Jan 2025.

Builds on

4 of 21 references.

References · 4

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2019How to Fine-Tune BERT for Text Classification?cited by 409
  4. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 359

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Multilingual Sentiment Analysis of Summarized Texts: A Cross-Language Study of Text Shortening Effectscited by 1
  2. 2025GS_DravidianLangTech@2025: Women Targeted Abusive Texts Detection on Social Mediacited by 0
  3. 2025Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysiscited by 0