yiru-jiao/kansei-knowledge-extraction-through-ltp

This repository provides code for the study "A proposal for Kansei knowledge extraction method based on natural language processing technology and online product reviews".

Code for "A proposal for Kansei knowledge extraction method based on natural language processing technology and online product reviews"

Each .py file is used to realize the function as its name indicates. To run all the codes without error, python version needs to be higher than py3, and the following libraries need to be installed： numpy, pandas, and pyltp(https://github.com/HIT-SCIR/pyltp)

Citation

Yiru Jiao, Qing-Xing Qu*. (2019). A proposal for Kansei knowledge extraction method based on natural language processing technology and online product reviews. Computers in Industry, 108, 1-11.

Abstract

With the rapid development of the economy, product design has gradually shifted to emotional design that focuses on satisfying users’ emotional needs. Kansei engineering is the commonly used method in product emotional design, the first and vital stage of which needed to be addressed is the acquisition of Kansei knowledge. Considering the development of natural language processing technology and online shopping, a computerized method to extract Kansei knowledge from online product reviews is firstly proposed in this article, and a relational extraction method to establish the relationship between product features and user perceptions is further provided. This article analyzes and extracts the Kansei words of 10 mice respectively using the proposed computerized method, taking the mouse as the case study. Then three evaluation indicators including diversity, effectiveness, and concentration are defined to assess the method, which evaluates the superiority with the advantage of 19.03% in diversity, 6.91% in effectiveness, 22.18% in the concentration and 8.9 times higher in the total score compared with traditional method. Furthermore, taking the best-selling mouse for example, the relational extraction method is applied to extract the relationship between the user concern and the user attitude, establish the relational table, draw Kansei knowledge tree, and finally model connection between product features and user perceptions. By utilizing natural language processing technology and integrating Kansei engineering, linguistics and computer science, it could be considered that the results of this article can accelerate the traditional user survey process, clarify users’ emotional needs, guide the adjustment of product design, and assist the user-centered product emotional design.