Software feature refinement prioritization based on online user review mining

2019 ◽  
Vol 108 ◽  
pp. 30-34 ◽  
Author(s):  
Jianzhang Zhang ◽  
Yinglin Wang ◽  
Tian Xie
Keyword(s):  
2014 ◽  
Vol 9 (2) ◽  
pp. 187 ◽  
Author(s):  
Takayuki Suzuki ◽  
Kiminori Gemba ◽  
Atsushi Aoyama

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imam Salehudin ◽  
Frank Alpert

PurposeWorldwide In-app Purchase (IAP) revenues reached almost US$37 billion in 2017 and doubled that in 2020. Although the revenue from IAPs exceeds those from paid apps, only 5% of total app users make any IAPs. This paper investigates why some users will not make IAPs and develop a novel concept of users' Perceived Aggressive Monetization of IAPs as an alternative framework to explain IAP behavior.Design/methodology/approachGiven the newness of IAPs, this study uses qualitative research to understand the phenomenon and develop a model to explain the decision to spend on IAPs. In total, this study collected 4,092 unique user-generated comments from app user review sites and social media webpages where users discuss in-app purchasing.FindingsThe analysis reveals recurring themes that explain user unwillingness to make in-app purchases, such as conflicting meanings of free-to-play, perceived unfairness and aggressive monetization of IAP by app publishers, and self-control issues. Subsequent user interviews support the themes and suggest that IAP spending might be more impulsive.Originality/valueThe paper develops a new concept of perceived aggressive monetization. Additionally, it proposes a novel theoretical framework that future researchers can use to understand why some mobile game users are unwilling to pay for IAPs.


2015 ◽  
Vol 13 (3) ◽  
pp. 137-152 ◽  
Author(s):  
Feng Wang ◽  
Li Chen
Keyword(s):  

2018 ◽  
Vol 65 (11) ◽  
pp. 1537-1569 ◽  
Author(s):  
Jessica Huff ◽  
Danielle Wallace ◽  
Courtney Riggs ◽  
Charles M. Katz ◽  
David Choate

Although massage parlors have been associated with illicit activities including prostitution, less is known about their association with neighborhood crime. Employing the Computer Automated Dispatch/Record Management System (CAD/RMS), online user review, licensing, Census, and zoning data, we examine the impact of massage parlors on crime in their surrounding neighborhoods. Using spatial autoregressive models, our results indicate the total number of massage parlors was associated with increased social disorder. The presence of illicit massage parlors in adjacent neighborhoods was associated with crime and physical disorder in the focal neighborhoods. This study has consequences for how police address crime associated with massage parlors. Specifically, the use of online user review forums could be an effective way to identify illicit massage parlors. Recommendations for policing and code enforcement are discussed.


To find an appropriate doctor who is specialized to treat a certain disease while only symptoms are known is not easy job for the patients. In this paper, we describe a recommended framework to find the best doctors in accordance with patients' requirements. In the proposed system, first it considers only those doctors whose profile match with patients' requirements. Second, the best doctors will be recommended out of previously obtained doctors based on the parameter patients' feedback i.e., patients' review. Our proposal will suggest a doctor recommendation system that uses review mining technique, which can be used in those countries that have huge uneven distribution of medical resources. In our model we have used the decision tree for symptoms to disease mapping and Naive Bayes classifier for sentiment analysis which are connected to each other using a bridge of python logic and the required output is top doctors based on the users input


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