A Cross-Product Analysis of Earphone Reviews Using Contextual Topic Modeling and Association Rule Mining

Abstract

Within the evolving field of sentiment analysis, the integration of topic modeling and association rule mining presents a promising yet underexplored method. This approach currently lacks an organized framework for maximizing insights that aid in drawing robust conclusions concerning customer sentiments. Therefore, this study addresses the need and rationale for having comprehensive sentiment analysis systems by integrating topic modeling and association rule mining to analyze online customer reviews of earphones sold on Amazon. It employs Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic), a technique that generates coherent topics by effectively capturing contextual information, and Frequent Pattern Growth (FPGrowth), an efficient association rule mining algorithm used for discovering patterns and relationships in a dataset without candidate generation. This analysis of reviews on ten earphone products identified key customer concerns as follows: sound quality, noise cancellation, durability, and battery life. The results indicate an overall positive sentiment towards sound quality and battery life, mixed reviews on noise cancellation, and significant dissatisfaction with product durability. Using integrated topic modeling and association rule mining offers deeper insights into customer preferences and highlights specific areas for product improvement and guiding targeted marketing strategies. Moreover, we focused on algorithm selection to improve the model's performance and efficiency, ensuring effective compatibility with our sentiment analysis framework. This study demonstrates how combining advanced data mining techniques and structuring insights from written customer feedback enhances the depth and clarity of sentiment analysis, furthering its applicability in e-commerce research.