Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysis

• URL: http://arxiv.org/abs/2411.00691v1
• Date: Fri, 01 Nov 2024 15:52:09 GMT
• Title: Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysis
• Authors: Linda Zeng,
• Abstract summary: Code-mixing (CM) is prevalent in multilingual societies but poses challenges for natural language processing. We propose using a large language model to generate synthetic CM data, which is then used to enhance the performance of task-specific models.
• Score: 0.0
• License:
• Abstract: Code-mixing (CM), where speakers blend languages within a single expression, is prevalent in multilingual societies but poses challenges for natural language processing due to its complexity and limited data. We propose using a large language model to generate synthetic CM data, which is then used to enhance the performance of task-specific models for CM sentiment analysis. Our results show that in Spanish-English, synthetic data improved the F1 score by 9.32%, outperforming previous augmentation techniques. However, in Malayalam-English, synthetic data only helped when the baseline was low; with strong natural data, additional synthetic data offered little benefit. Human evaluation confirmed that this approach is a simple, cost-effective way to generate natural-sounding CM sentences, particularly beneficial for low baselines. Our findings suggest that few-shot prompting of large language models is a promising method for CM data augmentation and has significant impact on improving sentiment analysis, an important element in the development of social influence systems.

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