Multiple affective attribute classification of online customer product reviews: A heuristic deep learning method for supporting Kansei engineering

Abstract Kansei engineering is a product design methodology that takes into account the affective needs of customers and translates them into product parameters. Traditionally, it uses questionnaire survey to collect and understand affective responses of customers. However, this process requires active involvement of participants, which is expensive, time-consuming, knowledge intensive and labor labour intensive. Therefore, it is only suitable for relatively small-scale operations. Today, due to the rapid development of e-commerce and social media, customers often write reviews to describe their feelings and comments after purchasing a product. A larger number of customer product reviews are available on the Internet, providing free, trustworthy, and growing data. Recently, many studies have used opinion mining and sentiment analysis to analyze analyse customer product reviews. However, they focus primarily on product feature extraction or classification from a single perspective (i.e. good or bad). Research on multiple affective attributes has received less attention. In this paper, we propose a heuristic deep learning method that extracts affective opinions from customer product reviews, and classify them into seven pairs of affective attributes (i.e. like-dislike, aesthetic-inaesthetic, soft-hard, small-big, useful-useless, reliable-unreliable, like–dislike, aesthetic–inaesthetic, soft–hard, small–big, useful–useless, reliable–unreliable, and recommended-not recommended–not recommended). An experiment has been conducted by using the product reviews available in Amazon.com. The results show that the accuracy of the proposed method is above 86%, which outperforms the baseline methods. The extracted information has the potential to help both customers and product designers understand the customer comments from an affective perspective and assist them in making decisions.