Product Aspect Ranking and Its applications

Abstract

Customer feedback offers a wealth of valuable information for both businesses and consumers. However, this information is often unstructured, making it challenging to interpret and derive meaningful insights. This study introduces a methodology for product aspect ranking that leverages online customer reviews to automatically identify key aspects of products, thereby enhancing the utility of the feedback. The approach is guided by two primary observations: (1) significant product aspects are frequently mentioned by a large number of customers, and (2) consumer sentiment regarding these aspects has a notable impact on the overall product evaluation.

The proposed framework employs a shallow dependency parser to extract product aspects from a corpus of reviews and utilizes sentiment analysis to quantify user opinions on these aspects. A probabilistic aspect ranking algorithm is then developed, which assesses both the frequency of mentions and the influence of consumer sentiment for each aspect on the overall evaluation. This dual evaluation allows for the determination of the relative importance of various product aspects. The efficacy of the proposed methodology is demonstrated through empirical analysis conducted on a review corpus of 19 popular products. Furthermore, the application of product aspect ranking is extended to two real-world scenarios: document-level sentiment segmentation and review analysis and synthesis. The findings indicate significant performance improvements in these applications, highlighting the potential of aspect ranking to align with practical use cases effectively.