LLM for Sentiment Analysis in E-commerce: A Deep Dive into Customer Feedback
DOI:
https://doi.org/10.5281/zenodo.12730477Keywords:
bert, llm, gpt, deep learning, machine learning, nlpAbstract
With the rise of online shopping becoming an integral part of daily life, it has brought unparalleled convenience, allowing us to purchase anything from daily necessities to luxury items with ease. On platforms like Amazon[1], customer feedback mechanisms play a crucial role in shaping user behavior and business practices. These mechanisms include the Star Rate (1-5) and detailed reviews. The star rating provides a quick and intuitive way for customers to score a product, while reviews offer comprehensive descriptions and shopping experiences. These feedback systems influence other customers' purchasing decisions and provide valuable insights for businesses to improve their products. In our study, we explore the impact of time on user behavior by combining three datasets for analysis. We observe a macro trend of increasing online shopping activity and customer satisfaction over time, with a growing tendency for customers to leave feedback. From a micro perspective, we conduct time series analysis and establish that customer star ratings vary over time, forming a column relationship, which we model using the ARIMA[2] technique to predict future trends. Furthermore, we investigate the influence of reviews on customer responses, finding that negative reviews spread rapidly and widely through social networks, akin to a viral phenomenon. Utilizing the SIR virus model as a text-based network propagation model, we demonstrate the significant impact of negative reviews on consumption patterns.[3] Lastly, we employ advanced deep learning models such as BERT, LLM, and GPT for natural language processing (NLP) to analyze the sentiment in customer reviews. By leveraging word vectors through classification functions, we distinguish between pessimistic and optimistic emotions. Our findings reveal a higher-order functional relationship[4] between star ratings and these emotions, providing deeper insights into customer sentiment and product perception.Downloads
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Copyright (c) 2024 Zeyue Wang, Yue Zhu, Shuyao He, Hao Yan, Ziyi Zhu
This work is licensed under a Creative Commons Attribution 4.0 International License.
