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| A Kansei Engineering Integrated Approach for Customer-needs Mining from Online Product Reviews |
| Jia Danping, Jin Jian, Geng Qian, Deng Siyu |
| School of Government, Beijing Normal University, Beijing 100875 |
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Abstract In a fiercely competitive market, companies often have to optimize products and marketing strategies according to customer requirements (CRs). Rapid development of an economy increases the demand for a higher level of product improvement to address customers’ emotional needs. Aside from functional features, products are required to have emotional aesthete designs, making it important to identify emotional CRs. Kansei engineering is a mechanism that connects customer emotions with product features to reveal emotional CRs. Accordingly, the word2vec model and a sliding window technique are used in this study to semi-automatically generate a domain Kansei lexicon and a product feature dictionary. Additionally, a feature-Kansei model is developed to better represent CRs. To evaluate the effectiveness of the proposed model, an empirical case study is conducted using iPhone reviews. Compared with sentiment dictionary-based approaches, the integration of sentiment analysis and Kansei engineering helps to capture emotional CRs from online opinions effectively and provides more critical insights for decision-makers.
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Received: 04 April 2019
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