mlsanityData to 7 Oct 2026

Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon

arXiv 2402.02113 ↗First posted 3 Feb 2024cs.CLcited by 7

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Abstract (arXiv)

Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages. In this paper, we relax the reliance on texts in low-resource languages by using multilingual lexicons in pretraining to enhance multilingual capabilities. Specifically, we focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets. We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets, and large language models like GPT--3.5, BLOOMZ, and XGLM. These findings are observable for unseen low-resource languages to code-mixed scenarios involving high-resource languages.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 30 of its 70 references that are in the map. First posted on arXiv 3 Feb 2024.

Builds on

30 of 70 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

  1. 2024Large Language Models Meet NLP: A Surveycited by 102
  2. 2025SinLlama -- A Large Language Model for Sinhalacited by 8
  3. 2024Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settingscited by 6
  4. 2024Large Language Models in Targeted Sentiment Analysiscited by 3
  5. 2025Benchmarking Open-Source Large Language Models for Persian in Zero-Shot and Few-Shot Learningcited by 1
  6. 2025TriLex: A Framework for Multilingual Sentiment Analysis in Low-Resource South African Languagescited by 0
  7. 2026Finding Meaning in Embeddings: Concept Separation Curvescited by 0