• ETRI Journal
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• v.43 no.1
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• pp.95-108
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• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.57-62
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• 2022

The Sambodana project is a mobile communication security system development project using Hardening Android. The initial idea for this project is that information leakage occurs outside of a communications application with end-to-end cryptographic security. Android hardening prevents unwanted applications and bloatware from being installed, such as unavailable Google Play Store or install restrictions.

• Smart Media Journal
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• v.12 no.6
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• pp.27-34
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• 2023

As Netflix usage has increased due to the COVID-19 pandemic, users' experiences with the service have also increased. Therefore, this study aims to conduct topic modeling analysis based on Netflix review data to explore the changes in Netflix user experience and service before and after the COVID-19 pandemic. We collected Netflix app review data from the Google Play Store using the Google Play Scraper library, and used topic modeling to examine keyword differences between app reviews before and after the pandemic. The analysis revealed four main topics: Netflix app features, Netflix content, Netflix service usage, and Netflix overall reviews. After the pandemic, when user experience increased, users tended to use more diverse and detailed keywords in their reviews. By using Netflix review data to analyze users' opinions, this study shows the changes in user experience of Netflix services before and after the pandemic, which can be used as a guide to strengthen competitiveness in the competitive OTT market.

• International Journal of Contents
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• v.14 no.3
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• pp.46-52
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• 2018

The objective of this study was to determine the status of star chart game applications in the Google play store in Korea. The share of game genres in paid and free star charts of game applications was searched. Also, the average reviewer's rating, average number of reviews, and average age rating based on the genre of paid and free star charts of game applications, and the average price of paid applications based on genre were analyzed. Hypothesis tests for the differences in average reviewer's rating, average number of reviews, average age rating according to the genre of game applications were performed. Also, hypothesis tests for the differences in average reviewer's rating, average number of reviews, average age rating between the paid and free game applications along with the hypothesis test for the differences in price according to the genre of paid game applications were performed. Lastly, hypothesis tests for the correlation between the start chart ranking and number of reviews in association with the correlation between the start chart ranking and reviewer's rating were performed. Statistically significant differences in average reviewer's rating, average number of reviews, average age rating according to the genre of game applications, and between the paid and free game applications were verified. However, the correlation between the start chart ranking and number of reviews in association with the correlation between the start chart ranking and reviewer's rating were not statistically significant.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.11
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• pp.779-788
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• 2013

With a wide dissemination of smartphones, the number of native applications developed and sold freely by anyone is growing now. The application market activated by Apple's App Store is spreading more rapidly with Google's Google Play. But due to platform-dependent of native application's attribute, developers are programming at each platform. As a result, development cost is increasing compared to earnings. To solve a dependency problem, people focused on web application developed by web-based language. However, stores at each browser are requiring a web application to follow manifest format. And this causes browser-dependent problem. Those problems can be solved by installing a certain browser, but this can make an application useless on the other browser of a store. Dependency problem can narrow not only user's application variation, but also concentration on some specific store. OWS(Open Web Store) is a standard store that supports various web environments. It overcomes browser or platform dependency problems by interworking applications between stores. Also customers are able to choose a large number of applications. In this paper, related to OWS, I would like to suggest manifest standards and store's reference architecture. An interworking scenario is going to be proposed as well.

• Journal of Digital Convergence
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• v.18 no.1
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• pp.309-314
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• 2020

In this study, look into the future prospects of mobile games produced using IP, in the domestic market and to present more effective IP selection criteria when producing new mobile games using IP. To this end, classified IP-enabled mobile games and analyzed IPs used for IP-enabled mobile games by each type for games that are located in the top 200 in sales ranking of Google Play Store. According to the analysis, the mobile game using IP has shown significant performance in terms of sales performance, and the trend will continue in 2020. It was analyzed that 'Game' types of IP using games(based on domestic games) can achieve the highest sales results. Since the success of a game affects a variety of factors other than IP, further research is needed in more diverse ways on the impact of IP on the success.

• Human Ecology Research
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• v.58 no.4
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• pp.613-633
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• 2020

This study identifies consumers' perceived benefits and costs when using Samsung Health (a healthcare app) based on consumer reviews from Google Play Store's app and social media discourse. We examine the differences in the benefits and the costs of Samsung Health using these two sources of data. We conducted text frequency analysis, clustering analysis, and semantic network analysis using R programming. The major findings are as follows. First, consumers experience benefits and costs on several functions of the app, such as step counting, device interlocking, information acquisition, and competition with global consumers. Second, the results of semantic network analysis showed that there were eight benefit factors and three cost factors. We also found that the three costs correspond to the benefits, indicating that some consumers gained benefits from certain functions while others gained costs from the same functions. Third, the comparison between consumer app review and social media discourse showed that the former is appropriate to assess the performance of app functions, while the latter is appropriate to examine how the app is used in daily life and how consumers feel about it. The current study suggests managerial implications to healthcare app service providers regarding what they should strengthen and improve to enhance consumers' satisfaction. It also suggests some implications from the two media, which can be mutually complementary, for researchers who study consumer opinions.

• Journal of Korean society of Dental Hygiene
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• v.19 no.4
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• pp.493-502
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• 2019

Objectives: This study aimed to investigate the current status of oral health applications developed for smartphones because they can be used as a new educational medium to manage and improve oral health. Methods: This study examined 60 basic oral health applications provided by Google Play Store and Apple App Store as of May 2019 and examined delivery contents, delivery methods, application types, and other information. Results: Apple included 65.4% of oral apps in the game category whereas Android included 64.3% in the education category (p>0.05). All Apple's apps and 71.4% of Android apps were developed overseas (p<0.01). The delivery contents were 61.5% for Brushing + tooth decay in Apple, and 78.6% for others (oral care products and gum diseases) in Android (p>0.05). For the delivery method, game + video was 65.4% in Apple, and game and other methods (text, image, augmented reality) was 42.9% in Android (p>0.05). In the case of application type, play type was the most common with 88.5% in Apple, and 46.4% play type and 39.3% other type (text, appreciation, problem-solving types) in Android (p<0.01). In addition, play type was high in both education (53.8%) and game (90.0%) categories (p>0.05). The average review score was 4.30 in the education category, 4.34 in the case of brushing and care (delivery contents), 4.37 in the case of using game + video (delivery methods), and 4.57 in the case of Play + other types (application type) (p>0.05). Conclusions: The use of healthcare apps is expected to increase owing to improved lifestyles, an increase in the elderly population, cost-effectiveness, and convenience that is not affected by time and place. Effective use of oral health apps will require the participation of dental professionals in the development process to identify the exact status, expand subjects, and provide appropriate information.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.445-456
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• 2024

This study aims to suggest service improvement opportunities by analyzing user review data of the top three OTT service apps(Netflix, Coupang Play, and TVING) on Google Play Store. To achieve this objective, we proposed a framework for uncovering service opportunities through the analysis of negative user reviews from OTT service providers. The framework involves automating the labeling of identified topics and generating service improvement opportunities using topic modeling and prompt engineering, leveraging GPT-4, a generative AI model. Consequently, we pinpointed five dissatisfaction topics for Netflix and TVING, and nine for Coupang Play. Common issues include "video playback errors", "app installation and update errors", "subscription and payment" problems, and concerns regarding "content quality". The commonly identified service enhancement opportunities include "enhancing and diversifying content quality". "optimizing video quality and data usage", "ensuring compatibility with external devices", and "streamlining payment and cancellation processes". In contrast to prior research, this study introduces a novel research framework leveraging generative AI to label topics and propose improvement strategies based on the derived topics. This is noteworthy as it identifies actionable service opportunities aimed at enhancing service competitiveness and satisfaction, instead of merely outlining topics.

• The Journal of Information Systems
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• v.27 no.3
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• pp.141-160
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• 2018

Purpose The purpose of this study is to verify mobile game users' perceived utilities about real (price) and virtual (Ruby) currencies, which are the major factors of in-app purchase (IAP) widely used as a business model in a mobile game industry. Design/methodology/approach By using sales volume and sales frequency data for products sold in an 'S' mobile game from the 'D' mobile game company, we conducted a conjoint analysis according to the procedure recommended by Bicheno (2009). Findings The results of this study indicate that (1) mobile game users have greater utility for a virtual currency (Ruby) than for a real currency (price), (2) mobile game users' utility for virtual (Ruby) and real (price) currencies varies depending on sales volume and sales frequency, as well as (3) mobile markets (e.g., Google Play Store vs. Apple App Store), and (4) mobile game users have greater utility for a virtual currency (Ruby) which can be used readily than later.