Chen, L.; Shang, S.; Wang, Y. Cross-Lingual Sentiment Analysis with MultiEmo: Exploring Language-Agnostic Models for Emotion Recognition. Preprints2024, 2024081639. https://doi.org/10.20944/preprints202408.1639.v1
APA Style
Chen, L., Shang, S., & Wang, Y. (2024). Cross-Lingual Sentiment Analysis with MultiEmo: Exploring Language-Agnostic Models for Emotion Recognition. Preprints. https://doi.org/10.20944/preprints202408.1639.v1
Chicago/Turabian Style
Chen, L., Shifeng Shang and Yawen Wang. 2024 "Cross-Lingual Sentiment Analysis with MultiEmo: Exploring Language-Agnostic Models for Emotion Recognition" Preprints. https://doi.org/10.20944/preprints202408.1639.v1
Abstract
Cross-lingual sentiment analysis is crucial for understanding and interpreting emotions expressed in text across diverse linguistic contexts. However, cross-lingual sentiment analysis faces challenges such as differences in emotional lexicons, data imbalance, and the need for multi-language sentiment normalization. In this study, we propose a novel approach to address these challenges by leveraging low-resource language training techniques to enhance cross-lingual sentiment analysis. Our method aims to bridge the gap in emotional lexicons by adapting sentiment analysis models to diverse linguistic contexts. Additionally, we tackle issues of data imbalance through innovative data augmentation strategies tailored to each language, and we introduce a multi-language sentiment normalization technique to ensure consistent sentiment interpretation across different languages. Our method achieved state-of-the-art results in twelve languages across four domains, demonstrating superior performance in both text-level and sentence-level sentiment analysis tasks. By conducting comprehensive evaluations across diverse linguistic contexts and domains, we showcase the versatility and effectiveness of our approach in achieving top-tier results in cross-lingual sentiment analysis.
Keywords
Cross-lingual sentiment analysis; data imbalance; data augmentation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
