A Feature-Oriented Sentiment Rating for Mobile App Reviews

Cunha, Washington; Viegas, Felipe; Alencar, Rafael Odon de; Mourão, Fernando; Salles, Thiago; Carvalho, Dárlinton Barbosa Feres; Gonçalves, Marcos André; Rocha, Leonardo

doi:10.1145/3178876.3186168

Cited by 54 publications

(29 citation statements)

References 31 publications

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“…Online reviews are the most frequently used type of dynamic data for eliciting requirements (53%), followed by micro-blogs (18%) and online discussions/forums (12%), software repositories Online reviews • Online reviews included app reviews, reviews compiled by experts, and online user reviews. Among the studies which used online reviews, a majority of the studies used app reviews as the sources of potential requirements (75%) [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Of them, 14 used app reviews from multiple distribution platforms such as Apple AppStore and Google Play to increase the level of generalizability, while eleven used those from a single distribution platform, and one did not specify the number of app distribution platforms • Of the studies which used online reviews, 17% (n = 6) extracted user reviews of software and video games [55], IoT products [56], compact cameras [57], internet security [58], Jira and Trello [59], and Jingdong.…”

Section: The Specific Types Of Dynamic Data Used For Automated Requirmentioning

confidence: 99%

“…Software features [52] and technically informative information from the potential requirements sources [64,86] were summarized, ranked, and visualized using word clouds. Luiz et al [49] summarized overall user evaluation of the mobile applications, their features, and the corresponding user sentiment polarity and scores in a single graphical interface. Oriol et al [89] implemented a quality-aware strategic dashboard, which has various functionalities (e.g., quality assessment, forecasting techniques, and what-if analysis) and allows for maintaining traceability of quality requirements generation and documentation process.…”

Section: Rule-based Clusteringmentioning

confidence: 99%

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Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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Requirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

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Section: The Specific Types Of Dynamic Data Used For Automated Requirmentioning

confidence: 99%

Section: Rule-based Clusteringmentioning

confidence: 99%

Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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show abstract

“…Correspondingly, in paper [6], the author proposed a framework for mobile application developers with which they may be capable of bring in modification on functions the ones are located negative primarily based on the end user's evaluation in their application. Their framework consisted of three main building blocks (i)subject matter modeling, (ii)sentiment analysis and (iii)summarization interface.…”

Section: Literature Surveymentioning

confidence: 99%

An Approach To Predict Software Application Success Using Voting Ensemble Method

Rathod¹,

Ugalmugle²,

Waghmare³

et al. 2020

ITM Web Conf.

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Mobile app distribution platform consisting of Google play store and Apple Store gets covered with several hundreds of new apps every day with many more enthusiastic developers working independently or in a crew to make them successful. With huge competition from all over the world, it is vital for a developer to recognize if he is proceeding in the proper direction or not. It is not like making a film wherein presence of famous celebrities increase the chance of success even earlier than the movie is released, it is not the case with developing apps. Since maximum Play Store apps are free, the revenue version is pretty unknown and unavailable as to how the in-app purchases, in- app advertisements and subscriptions make a contribution to the fulfillment of an app. Thus, a software’s success is normally decided through the wide variety of installs and the star ratings that it has acquired over its lifetime in place of the sales it generated. So in order to test if the app is assembly the expectancy of human beings we need a software that will check that apps evolved are successful or not. The framework for predicting success of software application is proposed in this paper. It is a software which will provide the achievement of app/software program relying not only on number of install and star rating but will consider all the factors of an application description and customers reviews. The exploratory data analysis to jump in deeper into the Google Play Store information is performed. The relationships with specific functions inclusive of how the wide variety of phrases in an app call for instance, affect installs are used to use them to find out which apps are much more likely to succeed. Using those extracted functions and the of sentiment of customers the proposed method will predict the “success” of an application using Google Play Store Data. The algorithm applied are Support Vector Machine, K-Nearest Neighbour, Decision Tree and Random Forest. In order to improve accuracy the Voting Ensemble technique is applied in proposed method. The accuracy of various algorithm is compared to judge the performance of proposed method. Keywords : hyperplane, dataset, Support Vector Machine, Exploratory Data Analysis, voting ensemble

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“…Feita a coleta, uma etapa fundamental é pré processar esses artigos, removendo caracteres especiais, números e stopwords. Além disso, propomos também a remoção de adjetivos e advérbios, já que os termos mais importantes para identificar um tópico são verbos e substantivos [Luiz et al 2018]. Por fim, restringimos os termos utilizados ao conjunto de palavras do sistema de classificação da ACM, uma vez que pretendemos encontrar tópicos que se relacionem diretamente com áreas de estudo da Ciência da Computação.…”

Section: Etapa 1: Coleta E Representação Dos Dadosunclassified

“…No nosso caso, propomos utilizar a matriz W para abstrair a relação entre termos e tópicos e a matriz H a relação documentos e tópicos. A ideia central por trás do NMF é aproximar as colunas de Z por combinações lineares não negativas dos vetores base (colunas em H) [Luiz et al 2018]. Assim como em vários algoritmos de clustering, a escolha do k (i.e., do número de tópicos) é um problema, pois não existem garantias de encontrar a solução ótima global [Lin 2007].…”

Section: Etapa 2: Modelagem De Tópicosunclassified

An overview of Brazilian researches in the Computer Science field in last years

Peres¹,

Cecilio²,

Rodrigues³

et al. 2019

Anais Do VII Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2019)

Self Cite

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Recently, most traditional market services have joined online service platforms. Despite the practicality achieved, such services eventually bring a large amount of data to the Web. In this sense, data analysis, data engi- neering, and data science activities have become extremely necessary. In general, they can extract extra information about systems and users, allowing the owners to produce insights and analyze patterns. Then, we propose an evalua- tion methodology to be applied in the online scenario of registration of publications and scientific productions, such as ResearchGate and Lattes Platform of CNPq. This methodology is unsupervised and divided into three main stages: (i) obtaining and representing the data; (ii) application of topic modeling; and (iii) the labeling of topics. This proposal diverges from the literature’s proposes that are based on collaborative networks and supervised techniques. We applied this methodology to a Lattes database and were able to observe the evolution of Computer Science research in Brazil. Based on this analysis, it is possible to identify the most popular and least explored research lines in order to direct public investments according to a certain interest.

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A Feature-Oriented Sentiment Rating for Mobile App Reviews

Cited by 54 publications

References 31 publications

Data-Driven Requirements Elicitation: A Systematic Literature Review

Data-Driven Requirements Elicitation: A Systematic Literature Review

An Approach To Predict Software Application Success Using Voting Ensemble Method

An overview of Brazilian researches in the Computer Science field in last years

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