Multilingual Sentiment Analysis

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10 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Concept Direction Reliability Across Languages with Different Tokenizer Fertility
Sep 28, 2026Muhammad Abdullahi Said, Abass Oguntade, Elisha Komolafe +3Multilingual Sentiment Analysis Cross-Lingual Consistency in Language Models
African Institute for Mathematical Sciences · University of Cape Town · Bayero University Kano +2
Language-Specific Sentiment Polarity Biases in Encoder and Large Language Model Classification of Product Reviews
Jun 22, 2026Advita Rajiv, Kavitha Kothur, Gautham ReddyMultilingual Language Model Evaluation LLM Evaluation
John P. Stevens High School, Edison, NJ 08820 · Ford Foundation, New York, NY 10017 · NC State University, Raleigh, NC 27606
ICT-NLP at SemEval-2026 Task 3: Less Is More -- Multilingual Encoder with Joint Training and Adaptive Ensemble for Dimensional Aspect Sentiment Regression
May 11, 2026Liyuan Huang, Jiawei He, Wutao Shen +2Aspect-Based Sentiment Analysis Multilingual Sentiment Analysis
1State Key Laboratory of AI Safety · Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences
100,000+ Movie Reviews from Kazakhstan: Russian, Kazakh, and Code-Switched Texts
May 9, 2026Rustem YeshpanovMultilingual Language Model Evaluation Multilingual Sentiment Analysis
Independent Researcher / Astana, Kazakhstan
Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis
Apr 29, 2026Jakob Fehle, Nils Constantin Hellwig, Udo Kruschwitz +1Multilingual Language Model Evaluation Aspect-Based Sentiment Analysis
Media Informatics Group, University of Regensburg, Regensburg, Germany · Information Science Group, University of Regensburg, Regensburg, Germany
RoIt-XMASA: Multi-Domain Multilingual Sentiment Analysis Dataset for Romanian and Italian
Apr 18, 2026Andrei-Marius Avram, Aureliu Valentin Antonie, Cosmin-Mircea Croitoru +2Domain-Adversarial Learning Unsupervised Domain Adaptation
National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania
NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regression
Apr 10, 2026Tong Wu, Nicolay Rusnachenko, Huizhi LiangMultilingual Sentiment Analysis Sentiment Analysis
Independent Researcher · Centre for Applied Creative Technologies (CFACT+), Bournemouth University, UK · School of Computing, Newcastle University, Newcastle upon Tyne, UK
LogSigma at SemEval-2026 Task 3: Uncertainty-Weighted Multitask Learning for Dimensional Aspect-Based Sentiment Analysis
Mar 26, 2026Baraa Hikal, Jonas Becker, Bela GippMultilingual Sentiment Analysis Sentiment Analysis
University of Göttingen, Germany · LKA NRW, Germany
A Multi-Model Approach to English-Bangla Sentiment Classification of Government Mobile Banking App Reviews
Mar 17, 2026Md. Naim Molla, Md Muhtasim Munif Fahim, Md. Binyamin +4Multilingual Sentiment Analysis Sentiment Analysis
Data Science Research Lab, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh · Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh · Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance
Aug 26, 2020Selim F. Yilmaz, E. Batuhan Kaynak, Aykut Koç +2Multilingual Sentiment Analysis Sentiment Analysis
Department of Electrical and Electronics Engineering, Bilkent University, 06800 Ankara, Turkey · Department of Computer Engineering, Bilkent University, 06800 Ankara, Turkey · National Magnetic Resonance Center UMRAM, 06800 Ankara, Turkey +1

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Concept Direction Reliability Across Languages with Different Tokenizer Fertility

Muhammad Abdullahi Said, Abass Oguntade, Elisha Komolafe, Babangida Sani+2

African Institute for Mathematical Sciences · University of Cape Town · Bayero University Kano+2

Multilingual Sentiment Analysis Cross-Lingual Consistency in Language Models LLM Reliability Sentiment Analysis

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Extracted sentiment directions can vary across samples even when downstream sentiment classification remains accurate. To evaluate direction reproducibility, we measure split-half agreement in English, Hausa, and Yoruba representations across four language models using both native and translated texts. We identify layers selected for agreement using ten topics and evaluate direction agreement across separate groups of fifteen topics. Using the final token, split-half agreement ranges from 0.737 to 0.870 for English, 0.589 to 0.762 for Hausa, and 0.101 to 0.399 for Yoruba, maintaining this language rank order across all 77 complete model comparisons. Classifiers trained on these same layers consistently predict sentiment above chance, demonstrating that predictive accuracy does not imply directional consistency. Furthermore, averaging token representations yields less consistent agreement, and high agreement can partially reflect sentence length. Ultimately, our findings highlight the need to measure vector direction reproducibility independently of classification performance, though they do not establish that tokenizer fertility which is the average number of tokens per whitespace separated word causes cross-lingual differences.