| dc.description.abstract | In the fast-paced world of internet information flow, many traditional commercial behaviors have shifted towards online marketing models. Consumers, when making purchasing decisions, are often unable to effectively evaluate product quality and typically rely on internet and personal acquaintances′ reviews and hands-on tests. Given that the authenticity of reviews on many online platforms cannot be thoroughly verified, this study aims to assist consumers in making purchasing decisions by analyzing historical consumer reviews.
Currently, many platforms provide spaces for consumer discussions on various products. Most of these platforms are centrally managed, and preferences for them vary across different demographics, leading to the use of diverse platforms. This study seeks to integrate cross-platform data to conduct sentiment analysis on product reviews, offering users a more objective indicator for decision-making analysis. Traditional natural language processing sentiment analysis methods, such as keyword identification, statistical methods, and lexical association, are not as effective for multi-category topics. This research adopts a more comprehensive sentiment analysis approach by utilizing large language models to address the multifaceted development of products.
Decentralized technology and smart contracts can more ensure the immutability of reviews, thereby making sentiment analysis indicators more trustworthy. Through smart contracts, centralized platforms can avoid the risk of human tampering, further enhancing the reliability of this decentralized application system. | en_US |