Investigating the relationship between price, rating, and popularity in the Blackberry World App Store

Finkelstein, Anthony; Harman, Mark; Jia, Yunyi; Martin, William H.; Sarro, Federica; Zhang, Yuanyuan

doi:10.1016/j.infsof.2017.03.002

Cited by 61 publications

(60 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular, it uses Natural Language Processing (NLP) to extract the features of apps from their textual descriptions. The definition of an app feature (as extracted by the Harman et al's process [11] [14]) is as follows: "A feature is a claimed functionality offered by an app, captured by a set of collocated words in the app description and shared by a set of apps in the same category." For example, "receive Facebook message" and "7-days weather forecast" are two features extracted from apps in the IM & Social Networking and Weather categories, respectively [14].…”

Section: Estimating Apps' Rating From Claimed Featuresmentioning

confidence: 99%

Customer Rating Reactions Can Be Predicted Purely using App Features

Sarro

Harman

Jia

et al. 2018

2018 IEEE 26th International Requirements Engineering Conference (RE)

Self Cite

View full text Add to dashboard Cite

In this paper we provide empirical evidence that the rating that an app attracts can be accurately predicted from the features it offers. Our results, based on an analysis of 11,537 apps from the Samsung Android and BlackBerry World app stores, indicate that the rating of 89% of these apps can be predicted with 100% accuracy. Our prediction model is built by using feature and rating information from the existing apps offered in the App Store and it yields highly accurate rating predictions, using only a few (11-12) existing apps for case-based prediction. These findings may have important implications for requirements engineering in app stores: They indicate that app developers may be able to obtain (very accurate) assessments of the customer reaction to their proposed feature sets (requirements), thereby providing new opportunities to support the requirements elicitation process for app developers.

show abstract

Section: Estimating Apps' Rating From Claimed Featuresmentioning

confidence: 99%

Customer Rating Reactions Can Be Predicted Purely using App Features

Sarro

Harman

Jia

et al. 2018

2018 IEEE 26th International Requirements Engineering Conference (RE)

Self Cite

View full text Add to dashboard Cite

show abstract

“…app, users often read through the reviews. Research found that ratings and reviews correlate with sales and download ranks (Harman et al, 2012;Pagano and Maalej, 2013;Svedic, 2015;Martin et al, 2016;Finkelstein et al, 2017). Stable numerous ratings lead to higher downloads and sales numbers.…”

mentioning

confidence: 99%

Towards understanding and detecting fake reviews in app stores

2019

View full text Add to dashboard Cite

App stores include an increasing amount of user feedback in form of app ratings and reviews. Research and recently also tool vendors have proposed analytics and data mining solutions to leverage this feedback to developers and analysts, e.g., for supporting release decisions. Research also showed that positive feedback improves apps' downloads and sales figures and thus their success. As a side effect, a market for fake, incentivized app reviews emerged with yet unclear consequences for developers, app users, and app store operators. This paper studies fake reviews, their providers, characteristics, and how well they can be automatically detected. We conducted disguised questionnaires with 43 fake review providers and studied their review policies to understand their strategies and offers. By comparing 60,000 fake reviews with 62 million reviews from the Apple App Store we found significant differences, e.g., between the corresponding apps, reviewers, rating distribution, and frequency. This inspired the development of a simple classifier to automatically detect fake reviews in app stores. On a labelled and imbalanced dataset including one-tenth of fake reviews, as reported in other domains, our classifier achieved a recall of 91% and an AUC/ROC value of 98%. We discuss our findings and their impact on software engineering, app users, and app store operators.

show abstract

“… Rating ∈ [1,5] and it can reflect the popularity of an app. Research works indicate that the product with higher rating have a greater chance to be used by new customers . Thus, we define deviation between the rating of an app and the average rating of the products in the domain as the quantified value of rating, which is able to describe the market trend of an app.…”

Section: Automated Mining Of An App Descriptionmentioning

confidence: 99%

App store mining for iterative domain analysis: Combine app descriptions with user reviews

Liu

et al. 2019

Softw Pract Exp

View full text Add to dashboard Cite

Summary Compared with traditional software, the domain analysis of apps is conducted not only in the early stage of software development to gain knowledge of a particular domain but also runs throughout each iteration of apps to help developers understand evolution trends of the domain for maintaining their competitiveness. In this paper, we propose an approach to analyze app descriptions combined with reviews in App stores automatically and construct a feature‐based domain state model (FDSM) in the form of state machine to support the domain analysis of apps. In FDSM, the domain knowledge up to a certain moment together is defined as a state. Initial state summarizes the high‐level knowledge by gaining topics of app descriptions, whereas each transition is generated based on the information gained within one period of time and describes the change from the current state to the next one. Furthermore, user opinions in reviews are introduced into the model to quantify the value of information for helping developers get key domain knowledge efficiently. To validate the proposed approach, we conducted a series of experiments based on Google Play. The results show that FDSM can provide valuable information for supporting domain analysis, especially in the evolution process of apps.

show abstract

Investigating the relationship between price, rating, and popularity in the Blackberry World App Store

Cited by 61 publications

References 10 publications

Customer Rating Reactions Can Be Predicted Purely using App Features

Customer Rating Reactions Can Be Predicted Purely using App Features

Towards understanding and detecting fake reviews in app stores

App store mining for iterative domain analysis: Combine app descriptions with user reviews

Contact Info

Product

Resources

About