A systematic literature review: Opinion mining studies from mobile app store user reviews

Genc-Nayebi, Necmiye; Abran, Alain

doi:10.1016/j.jss.2016.11.027

Cited by 228 publications

(138 citation statements)

References 11 publications

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“…We have followed a systematic review protocol [38,39] to write this paper, to reduce bias in literature included and to improve the comprehensiveness in our review activities. It is suggested that following an explicit review protocol can help define the source selection and search processes, quality criteria, and information synthesis.…”

Section: Review Protocol Used In This Reviewmentioning

confidence: 99%

A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists' opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years. Appl. Sci. 2019, 9, 3300 2 of 27 the travel. In order to utilize this user-generated content properly and further to meet the needs of tourists and promote the tourism industry, we need to analyze and exploit tourists' needs and opinions, and then identify the problems of tourism services or destinations, which has become a new path for tourism development. Besides, as tourism needs become increasingly personalized, visitors begin to pursue self-likeness, self-worth, and diversified travel experiences, and they are no longer willing to endure delays or waits. How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36]. Moreover, while aiming to make computers understand human language, NLP has become the essential tool for text data analysis and is undergoing fast-pace growing based on the applications of deep learning in word embedding, syntax analysis, machine translation, and text understanding. Machine learning-based NLP techniques have been widely used in tourism text analysis, with superior results [19,25]. In addition, due to its high capability for extracting selective and invariant features from texts, and its independency of prior knowledge and linguistic resources, deep learning has been reported to achieve higher performance than other approaches on many NL...

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Section: Review Protocol Used In This Reviewmentioning

confidence: 99%

A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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“…that projects with Kotlin attracted more stars. [11]; many studies focused on opinion mining from Play Store Reviews: Genc et al provide a systematic literature review of works leveraging such practice [12]. Other large-scale studies were based on all the Android applications whose source code was published on GitHub: in our previous work, we analyzed the scripted testing diffusion and evolution on a data set of 280,000 Android projects [13].…”

Section: Threats To Validitymentioning

confidence: 99%

Characterizing the transition to Kotlin of Android apps: a study on F-Droid, Play Store, and GitHub

Coppola

Ardito

Torchiano

2019

Proceedings of the 3rd ACM SIGSOFT International Workshop on App Market Analytics

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Context: Kotlin is a novel language that represents an alternative to Java, and has been recently adopted as a first-class programming language for Android applications. Kotlin is achieving a significant diffusion among developers, and several studies have highlighted various advantages of the language when compared to Java.Goal: The objective of this paper is to analyze a set of open-source Android apps, to evaluate their transition to the Kotlin programming language throughout their lifespan and understand whether the adoption of Kotlin has impacts on the success of Android apps.Methods: We mined all the projects from the F-Droid repository of Android open-source applications, and we found the corresponding projects on the official Google Play Store and on the GitHub platform. We defined a set of eight metrics to quantify the relevance of Kotlin code in the latest update and through all releases of an application. Then, we statistically analyzed the correlation between the presence of Kotlin code in a project and popularity metrics mined from the platforms where the apps were released.Results: Of a set of 1232 projects that were updated after October 2017, near 20% adopted Kotlin and about 12% had more Kotlin code than Java; most of the projects that adopted Kotlin quickly transitioned from Java to the new language. The projects featuring Kotlin had on average higher popularity metrics; a statistically significant correlation has been found between the presence of Kotlin and the number of stars on the GitHub repository.Conclusion: The Kotlin language seems able to guarantee a seamless migration from Java for Android developers. With an inspection on a large set of open-source Android apps, we observed that the adoption of the Kotlin language is rapid (when compared to the average lifespan of an Android project) and seems to come at no cost in terms of popularity among the users and other developers.

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“…Software repositories can be a valuable source of information since they contain (or may allow to extract) information about the technical and social perspectives of a software project, such as sources of developer communications (Genc-Nayebi & Abran, 2016). Mining Software Repositories (MSR) area focuses on uncovering useful information about software by extracting and analyzing data from different software repositories (Ahmed, 2008).…”

Section: Mining Questions and Answers Repositoriesmentioning

confidence: 99%

Supporting governance of mobile application developers from mining and analyzing technical questions in stack overflow

Fontão

Ábia

Wiese

et al. 2018

J Softw Eng Res Dev

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There is a need to improve the direct communication between large organizations that maintain mobile platforms (e.g. Apple, Google, and Microsoft) and third-party developers to solve technical questions that emerge during the project and development of developers' contributions in a Mobile Software Ecosystem (MSECO). In this context, those organizations may not know how to define and evolve strategies to govern their developers towards achieving their organizational goals. Such organizations use an infrastructure to support developers, for example, questions and answers (Q&A) portals such as Stack Overflow. Interactions among developers in these portals feed a Q&A repository that can serve as a mechanism to understand and define strategies to support developers. In this paper, we mined 1,568,377 technical questions from Stack Overflow related to Android, iOS, and Windows Phone platforms. Next, we performed comparisons among those MSECO regarding: (i) developers' activity intensity, (ii) hot-topics (using Latent Dirichlet allocation algorithm) from all and more commented/viewed questions, (iii) "What" and "How to" questions, (iv) hot-topics from more viewed unanswered questions, and (v) relationship among questions and official developer events. From the results, we identified four key insights: recruiting, educating, and monitoring strategies; barrier reduction; management of technology insertion; and fostering of relationships. The relevance of the four key insights to support developer governance was evaluated by practitioners through a survey. Finally, for each key insight we associated a total of 10 strategies to support developer governance activities. Such strategies were extracted from 65 studies identified through a systematic mapping of the literature. IntroductionIn Software Engineering, the relationship among mobile application developers and an organization responsible for a technological platform (keystone) which involves cooperation and competition has been investigated as a Software Ecosystem (SECO) (Bosch, 2009). In the mobile application (app) scenario, this context refers to a specific type of SECO, known as Mobile Software Ecosystem (MSECO) (Lin & Ye, 2009;Fontão et al., 2015). The developer is an essential actor to sustain MSECO contributions, such as apps and technical documentation (Fontão et al., 2016;Koch & Kerschbaum, 2014). Such contributions are usually stored in an official, internal MSECO repository, such as Android 1 and Apple 2 Developers repositories. Moreover, there are links between MSECO and external repositories, such as code (e.g. GitHub) (Casalnuovo et al., 2015) and questions and answers (Q&A) repositories. As an example of an external Q&A repository, Stack Overflow has a set of technical questions/answers that arise from the use of APIs, SDKs, and development tools (Ahmad et al., 2018). Such external repositories help to maintain the interaction among developers over a common platform, resulting in a set of contributions and influencing directly or indirectly the...

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A systematic literature review: Opinion mining studies from mobile app store user reviews

Cited by 228 publications

References 11 publications

A Review of Text Corpus-Based Tourism Big Data Mining

A Review of Text Corpus-Based Tourism Big Data Mining

Characterizing the transition to Kotlin of Android apps: a study on F-Droid, Play Store, and GitHub

Supporting governance of mobile application developers from mining and analyzing technical questions in stack overflow

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