Abstract
Users often show different and personalized product requirements with respect to their gender, age, location, and other attributes. The requirement analysis of various users is thus vitally important for product evolution and customization. Accordingly, we propose a product-and-user oriented approach for requirement analysis, which can automatically elicit, classify, and rank product requirements from online reviews, and identify requirement differences among users. More specifically, the product features are first extracted from online reviews in a machine-learning manner. Next, a hierarchical clustering model is applied for product feature classification, and a weighted function of customer sentiment and attention is put forward for requirement ranking. Then, statistical methods are used for requirement preference identification. Finally, the effectiveness and feasibility of the proposed approach is examined on 2198 online reviews with 148 527 subsentences about new energy vehicles.
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