移动APP演化策略研究

In the era of mobile Internet, a large number of APP users pay more attention to product experience and express their usage and suggestions through comments. Research on online comment data has become a hot topic, and user feedback from comments is conducive to APP evolution and upgrading. But comment mining for APP is in the ascendant. In this paper, a large number of user comment data are collected from 9 APP stores to screen the demand attributes and emotional tendencies contained in the comments. The KANO model is used for modeling and analysis, and the attributes are mapped to attractive quality, one-dimensional quality, must-be quality and other categories. According to the KANO category of the APP attribute, a reasonable and effective update evolution strategy is proposed: the APP developers should give priority to meeting the requirements of one-dimensional quality and must-be quality, and gradually realize the requirements of attractive quality. Finally, this paper proves the robustness and portability of the method.