Product Attribute and Heterogeneous Sentiment Analysis-Based Evaluation to Support Online Personalized Consumption Decisions

To effectively address challenges that stem from e-commerce, it is crucial to harness diverse review data from e-commerce platforms. These data support consumers in making informed purchase decisions and aid manufacturers in optimizing product attributes. Incorporating sentiment data from heterogeneous reviews across different time periods into a decision-making framework is a pivotal consideration in purchase decisions and product design. The goal of the study is to establish an online product decision support method grounded in consumer irrational behavior and segmented reviews over time. It aims to offer users reliable and consistent outcomes when making personalized purchase decisions. The probabilistic linguistic term set is employed to represent consumer sentiments with varying degrees of granularity across different time periods. Subsequently, stochastic sampling is utilized to simulate the decision-making process of individual consumers. Regret theory is then applied to analyze consumers' irrational psychological behavior. Building upon heterogeneous data gathered from e-commerce platforms, including review ratings, likes, and follow-up reviews, a multiperiod group decision approach based on maximum similarity and review helpfulness is proposed. This decision-making method is advanced through a decomposition-aggregation process, safeguarding against information distortion and ensuring result reliability. This method provides consumers with product selection solutions across the temporal dimension and serves as a theoretical compass for manufacturers and sellers seeking product enhancement and sales optimization.