Abstract
In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective.
Highlights
In this paper, based on online shopping and personal laptops, we propose a method of short text mining to quantify the user requirements and trend judgment of product design
Keim proposed a classification of information visualization and visual data mining technology [57]
We take online shopping as the main starting point and propose an improved short text mining method based on user reviews of shopping websites
Summary
Improving the research of specific text data mining based on Chinese to use the network text knowledge database effectively is the focus of this field. In this paper, based on online shopping and personal laptops, we propose a method of short text mining to quantify the user requirements and trend judgment of product design. The design of data visualization interaction is still carried out according to the experimental data The innovations of this method are as follows:. Based on Sogou input method’s Chinese emotional word class library and PFE algorithm, the number of product features and sentiment tendency expressions were obtained so as to test the feature support again and eliminate more meaningless “noun adjective” combinations.
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