Emotion Analysis with Lightweight Adaptation of LLaMA3-8b

Abstract

Emotion analysis plays a pivotal role in understanding human affective states through various modalities such as text, speech, and facial expressions. Recent advancements in deep learning, especially with transformer models like LLaMA3-8b, have significantly improved the accuracy and performance of sentiment and emotion detection tasks. This research presents a lightweight adaptation of the LLaMA3-8b model specifically tuned for emotion analysis. The lightweight adaptation involves optimizing the pre-trained LLaMA3-8b model, reducing the resource requirements, and enabling it to handle emotional tone and context in text more efficiently. The results indicate that the model's adaptation to emotion detection not only retains the performance of the base LLaMA model but also enhances its ability to detect nuanced emotional states. By evaluating this modified architecture on various emotion detection datasets, we demonstrate the benefits of this lightweight adaptation. The findings highlight the potential of fine-tuning large language models like LLaMA3-8b for specialized tasks like emotion analysis without the heavy computational overhead typically associated with such models. This study opens the door for more efficient and accessible emotion analysis tools in practical applications such as customer service, social media monitoring, and mental health assessments.