Rosa Arboretti, Manuel Barusco, Elena Barzizza, Nicolò Biasetton, Riccardo Ceccato, Davide Ferro, Luigi Salmaso, Giacomo Vezzosi
Abstract: In the digital age, online product reviews play a pivotal role in influencing consumer choices. This paper presents an innovative tool that combines state-of-the-art natural language processing and sentiment analysis technique with ChatGPT technology. The tool enables automated web scraping and comprehensive sentiment analysis of product reviews, with a focus on aspect-based sentiment analysis and document-level sentiment aggregation. Our tool employs ChatGPT, a powerful language model, to conduct aspect-based sentiment analysis, extracting nuanced sentiments related to specific product attributes. This provides a granular understanding of consumer opinions. The tool also integrates advanced models for document-level sentiment analysis, enabling the aggregation of overall sentiment scores from multiple reviews. The framework comprises three main components: Web Scraping Module: this automates data collection from two major e-commerce platforms ensuring systematic extraction of reviews and metadata. Aspect-Based Sentiment Analysis Module:n leveraging ChatGPT, it categorizes opinions as positive, negative, or neutral and identifies specific product attributes. Document-Level Sentiment Analysis Module: this component provides an overall sentiment rating for the product. The tool can find applications in business, research, and consumer decision-making, allowing real-time product performance monitoring, market studies, and informed purchase decisions. Our integrated framework offers a robust solution for understanding and responding to consumer sentiments in online product reviews. By combining aspect-based and document-level sentiment analysis with ChatGPT's capabilities, it facilitates comprehensive insights into the digital marketplace.
Keywords: ChatGPT, webscraping, sentiment analysis, NLP.
Date Published: August 23, 2024 DOI: 10.11159/jmids.2024.006
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