Modeling Kansei Index of Product by Machine Learning Using Review Text and Image

In the field of Kansei (affective) engineering, the approach is often taken of modeling product's Kansei index to meet user's affective needs. This study work on modeling Kansei index automatically by machine learning using review text and images of products on the web. The proposed method follows: (1) Extraction of the main impressions of target domain and calculation of text impression scores that express the strength of each impression from review text by text mining, (2) creation of the product image data set with the training label made from the distribution of evaluation to products' impression by human and (3) construction of the deep neural network that estimates image impression score of product using the data set. Wristwatches were applied to proposed method as target domain. Then, estimation accuracy of constructed deep neural network was verified. As a result, high correlation coefficient 0.67 was confirmed between image impression scores and text impression scores, and effectiveness of the proposed method was confirmed. In addition, since the result exceeded correlation coefficient 0.51 calculated from estimation result of another deep neural network which has not learned distribution of evaluation, it was shown that learning the distribution was effective for improving estimation accuracy.