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

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arxiv 2501.12540 v1 pith:BHDED54F submitted 2025-01-21 cs.CL

Mikhail Krasitskii , Olga Kolesnikova , Liliana Chanona Hernandez , Grigori Sidorov , Alexander Gelbukh This is my paper · ORCID
classification cs.CL
keywords languagessentimentanalysisxlm-raboveaccuracyachievingacross
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis

    cs.CL 2025-06 reject novelty 4.0 of 10

    The paper claims a hybrid TF-IDF plus XLM-R summarization pipeline improves multilingual sentiment accuracy to 0.90 in English while cutting compute, but provides no reproducible supporting evidence.

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