Bridging resource gaps in cross-lingual sentiment analysis: adaptive self-alignment with data augmentation and...

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Machine Learning

Background:

• Cross-lingual sentiment analysis is vital for understanding emotions across languages.
• Significant performance gaps exist for fewer-resource languages (low- and medium-resource).
• Existing methods struggle to address resource imbalances effectively.

Purpose of the Study:

• To propose an adaptive self-alignment framework for large language models (LLMs).
• To mitigate performance disparities in cross-lingual sentiment analysis for resource-scarce languages.
• To establish a new benchmark for multilingual sentiment analysis.

Main Methods:

• Developed an adaptive self-alignment framework for LLMs.
• Incorporated novel data augmentation techniques.
• Utilized transfer learning strategies to address resource imbalances.

Main Results:

• Achieved an average F1-score improvement of 7.35 points across 11 languages.
• Demonstrated superior performance compared to state-of-the-art baselines.
• Showcased exceptional effectiveness in fewer-resource languages, narrowing the performance gap.

Conclusions:

• The proposed framework significantly enhances cross-lingual sentiment analysis, particularly for low-resource languages.
• The approach offers robust domain adaptation capabilities for real-world applications.
• This research advances inclusive and equitable natural language processing solutions.