What People Like in Mobile Finance Apps

Huebner, Johannes; Frey, Remo Manuel; Ammendola, Christian; Fleisch, Elgar; Ilic, Alexander

doi:10.1145/3282894.3282895

Cited by 29 publications

(14 citation statements)

References 20 publications

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“…Mining these opinions involves identifying user sentiment about discussed topics (Gu and Kim 2015;Da ¸browski et al 2020), features (Guzman and Maalej 2014;Gunaratnam and Wickramarachchi 2020) or software qualities (Bakiu and Guzman 2017;Masrury and Alamsyah 2019;Franzmann et al 2020). These opinions can help software engineers understand how users perceive their app (Guzman and Maalej 2014;Gu and Kim 2015;Huebner et al 2018;Franzmann et al 2020), discover users' requirements (Da ¸browski et al 2019;Dalpiaz and Parente 2019) and preferences (Guzman and Maalej 2014;Bakiu and Guzman 2017; Table 8 Types of data that have been combined with app reviews using search and information retrieval…”

Section: Sentiment Analysismentioning

confidence: 99%

Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

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Section: Sentiment Analysismentioning

confidence: 99%

Analysing app reviews for software engineering: a systematic literature review

et al. 2022

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“…Based on the aspects of financial applications distinguished in the article by Huebner et al (2018), it was decided to include them in the analysis of investment applications, as a subset of applications from the financial area. The highlighted aspects of the application are the presence of advertisements, user interface, price list, memory/battery usage, compatibility, connection, privacy settings,…”

Section: Resultsmentioning

confidence: 99%

“…Reviews relating to errors of previous versions occur even though the product is released in subsequent releases. Analysis of reviews is particularly difficult if at some point there was a program defect that was reported by the application users (Huebner et al, 2018;. This makes it difficult to assess the application properly.…”

Section: Introductionmentioning

confidence: 99%

Overview of Financial Applications for Investing on the Stock Exchange - Regression Models and Sentiment Analysis

Probierz¹,

Gałuszka²,

Grzejszczak³

et al. 2022

ERSJ

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Purpose:The aim of the review is to analyze the available investment applications that can be downloaded by Google or Apple in terms of available functionality. Design/methodology/approach: Correlation and regression analyses were carried out on the obtained data. Next, aspects of the application that most often appear in reviews were distinguished and sentiment analysis was carried out. Findings: The results obtained indicate an extremely important share of the specified functionalities of the application in its global evaluation, and allow for the presentation of specified aspects both in terms of sentiment and evaluation of the application. Practical Implications: The results of the study could be useful for developers and consumers of financial applications. For the first, the analyses carried out can point to directions of development that are particularly relevant. For the recipients, it is important to indicate the wide range of possibilities offered by the applications. Originality/Value: The obtained results indicate the importance of analyzing individual application functionalities in order to understand the complexity of the problem of application evaluation in popular websites.

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“…In addition to these approaches, many other approaches for analyzing feedback from app stores exist. These approaches can mostly be differentiated by the form of sentiment analysis they apply or by the approach they use for topic modeling; examples are [16], [17], [18], and [19].…”

Section: Existing Approaches For Textual User Feedback Collection and Analysismentioning

confidence: 99%

Establishing Continuous App Improvement by Considering Heterogenous Data Sources

Scherr

Hupp

Elberzhager

2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract"><span lang="EN-US">Mobile apps have penetrated the market and are being used everywhere. Companies developing apps face increasing challenges such as short time to market or demand for high quality. Furthermore, the success of an app also depends on how users perceive its quality. Feedback provided by users influences other potential users and provides new opportunities for identifying features. Consequently, it is a valuable source of input for app developers with respect to product improvements. One form is textual feedback. This kind of feedback is usually distributed across various data sources. Therefore, it must be captured from these sources and put into one single pool of data before it can be analyzed. The analysis must take into account the peculiarities of the short release cycles and high change rate of features for mobile apps. In this article, we present User Echo Service (UES), which was built to address heterogeneous data sources. The aim of UES is to allow product managers to be able to be always up to date with the latest feedback data. Therefore, we have created an extensible architecture aimed at supporting different data sources and present the feedback collection scheduling system. This forms the prerequisite for subsequent analyses of the collected data. We discuss our solution and provide ideas for future development.</span></p>

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What People Like in Mobile Finance Apps

Cited by 29 publications

References 20 publications

Analysing app reviews for software engineering: a systematic literature review

Analysing app reviews for software engineering: a systematic literature review

Overview of Financial Applications for Investing on the Stock Exchange - Regression Models and Sentiment Analysis

Establishing Continuous App Improvement by Considering Heterogenous Data Sources

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