Sentiment Analysis of Product Reviews Using Fine-Tuned LLaMa-3 Model: Evaluation with Comprehensive Benchmark Metrics - Wang Yili (ITM Web of Conferences)
Sentiment analysis, a crucial subfield of natural language processing, enables businesses and policymakers to understand public emotions and opinions, essential for crafting effective strategies across industries like marketing and customer service. As the volume of online reviews grows, automated sentiment classification models have become vital for efficiently processing this data. This study explores fine-tuning the LLaMA-8B large language model based on the Amazon Product Reviews dataset from Kaggle, aiming to improve sentiment classification accuracy. Using the LoRA fine-tuning approach combined with the Variant Greedy Search Technique (VGST) and TextBlob for polarity handling, the research addresses dataset size challenges. The model’s fine-tuning process includes one-shot learning and chain-of-thought prompting to better capture nuanced sentiment expressions. Evaluated using comprehensive metrics, LLaMA-8B demonstrates superior precision compared to Qwen2-7B and achieves near LLaVA performance with enhanced speed. Additionally, it outperforms models like Decision Tree, SVM, Multinomial NB, and XLNet in accuracy. This work underscores the potential of large language models for sentiment analysis and sets the stage for future extensions to multimodal input scenarios.