Extraction of affective responses from customer reviews: an opinion mining and machine learning approach

ABSTRACT

Kansei Engineering (KE) is a user-oriented technology combing customer psychological feelings and engineering for designing and developing products. Conventionally, questionnaire surveys have been extensively applied for understanding customers’ affective demands, responses and evaluations. However, the questionnaire is usually time-consuming, labour-intensive and small in data size. Online customer reviews provide trustable, continuously updated and free customers’ responses. Existing studies generally focus on the polarity classification of the positivity and negativity of the review texts. This study proposes an opinion mining approach based on KE and machine learning to extract and measure users’ affective responses to products from online customer reviews. Five types of machine learning algorithms are applied, including Support Vector Machine (SVM), Support Vector Regression (SVR), Classification and Regression Tree (CART), Multi-Layer Perceptron (MLP) and Ridge Regression (RR). An experiment has been conducted to illustrate the proposed approach. The results show that SVM+SVR is the best performer. It achieved a recall, precision and $F_1$ score of more than 80% for the classification of the soft-hard attribute with the smallest mean square error. Based on the proposed method, designers and manufacturers can effectively know customers’ responses to products through inputting the review texts to facilitate the process of product design.

KEYWORDS:

• Kansei engineering
• affective responses
• customer reviews
• review annotation
• opinion mining

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by National Natural Science Foundation of China under Grant [51405089]; Science and Technology Planning Project of Guangdong Province under [2015B010131008 and 2015B090921007]; China Postdoctoral Science Foundation under [2018M630928]; and Natural Science Foundation of Guangdong Province under [2018A0303130035].