Online Product Review Analysis to Automate the Extraction of Customer Requirements

The increasing use of online retail platforms has generated an enormous amount of textual data on the user experiences with these products in online reviews. These reviews provide a rich resource to elicit customer requirements for a category of products. The recent research has explored this possibility to some extent. The study reported here investigates the coding of publically available user reviews to understand customer sentiments on environmentally-friendly products. The manual review typically consists of a qualitative analysis of textual content, which is a resource-intensive process. An automated procedure based on Aspect-Based Sentiment Analysis (ABSA) is proposed and explored in this study. This procedure can be beneficial in analyzing reviews of products that belong to a specific category. As a case study, environmentally-friendly products are used. Manual content analysis and automated ABSA-based analysis are performed on the same review data to extract customer sentiments. The results show that we obtain over a 50% classification accuracy for a multiclass classification NLP task with a very elementary word vector-based model. The drop in accuracy (compared to human annotation) can be offset because an automated system is thousands of times faster than a human. Given enough data, it will perform better than its human counterpart in tasks on customer requirement modeling. We also discuss the future routes that can be taken to extend our system by leveraging more sophisticated paradigms and substantially improving our system’s performance.