Main Article Content

Abstract

The project proposes to build a system that is capable of extracting business intelligence for a manufacturer, from online product reviews. For a particular product, it extracts a list of the discussed features and their associated sentiment scores. Online products reviews and review characteristics are extracted from www.Amazon.com. A two-level filtering approach is adapted to choose a set of reviews that are perceived to be useful by customers. The filtering process is based on the concept that the reviewer generated textual content and other characteristics of the review, influencing peer customers in making purchasing choices. The filtered reviews are then processed to obtain a relative sentiment score associated with each feature of the product that has been discussed in these reviews. Based on these scores, the customer's impression of each feature of the product can be judged and used for the manufacturer's benefit.

Keywords

product customer's purchasing choices business intelligence

Article Details

How to Cite
V, . S., & Rupa, . S. S. . (2021). Extracting Business Intelligence from Online Product Reviews. Convergence Chronicles, 2(2), 289–296. https://doi.org/10.53075/Ijmsirq/1293457975676996