Development and application of user review quality model for embedded system

Kasiviswanathan, Somasundaram; Ramalingam, Dillibabu

doi:10.1016/j.micpro.2020.103029

Cited by 2 publications

(1 citation statement)

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“…By mining and analyzing online review, Yu found out the user's consumption evaluation of each attribute of a specific model of mobile phone, which is convenient for consumers to understand the advantages and disadvantages of the various attributes of mobile phones, providing suggestions for merchants to improve their products. In the development of software products, Kasiviswanathan and Ramalirgam [15] used the Software User Review Defect Corrective (SURDCM) model for requirements analysis and prioritization, combined with Software failure mode and effects analysis (SWFMEA), a technique to ensure that the software development process was defect-free, to detect possible defects and their respective impacts. Xu et al [16] used text mining and other methods to connect customers' online text comments with customers' perceptions, helping business managers better understand customers' needs through User Generated Content (UGC).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of User Needs on Downloading Behavior of English Vocabulary APPs Based on Data Mining for Online Comments

et al. 2021

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With highly developed social media, English learning Applications have become a new type of mobile learning resources, and online comments posted by users after using them have not only become an important source of intellectual competition for enterprises, but can also help understand customers’ requirements, thereby improving product functionalities and service quality, and solve the pain points of product iteration and innovation. Based on this, this paper crawled the online user comments of three typical APPs (BaiCiZhan, MoMoBeiDanCi and BuBeiDanCi), through emotion analysis and hotspot mining technology, to obtain user requirements and then the K-means clustering method was used to analyze user requirements. Finally, quantile regression is used to find out which user needs have an impact on the downloads of English vocabulary APPs. The results show that: (1) Positive comments have a more significant impact on users’ downloads behavior than negative online comments. (2) English vocabulary APPs with higher downloads, both the 5-star user ratings and the increase of emotional requirement have a negative effect on the increase in APP downloads, while the enterprise’s service requirement improvement has a positive effect on the increase of APP downloads. (3) Regarding English vocabulary APPs with average or high downloads, improving the adaptability and Appearance requirements have significant negative impact on downloads. (4) The functional requirements to improve products will have a significant positive impact on the increase in downloads of English vocabulary APPs.

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Section: Literature Reviewmentioning

confidence: 99%