Advancing Text Emotion Classification via LLaMA3-8b and LoRA

Abstract

Text emotion classification plays a vital role in understanding human emotions expressed through written language, which is essential for a variety of applications in natural language processing (NLP), such as sentiment analysis, mental health monitoring, and customer feedback analysis. Recent advancements in large language models (LLMs) and fine-tuning techniques, such as Low-Rank Adaptation (LoRA), have paved the way for improved performance in tasks like emotion classification. In this research, we explore the potential of combining LLaMA3-8b, a state-of-the-art LLM, with LoRA for enhancing the accuracy and efficiency of text emotion classification. Our experiments show that the integration of LoRA significantly improves the performance of LLaMA3-8b, making it highly effective for handling complex and nuanced emotion classification tasks. This paper outlines the significance of LLaMA3-8b, the LoRA fine-tuning strategy, and their impact on advancing emotion classification in NLP.