Requirements Behind Reviews: How do Software Practitioners See App User Reviews to Think of Requirements?

ObjectivesOur goal was to gain insights into the user reviews of the three COVID-19 contact-tracing mobile apps, developed for the different regions of the UK: ‘NHS COVID-19’ for England and Wales, ‘StopCOVID NI’ for Northern Ireland and ‘Protect Scotland’ for Scotland. Our two research questions are (1) what are the users’ experience and satisfaction levels with the three apps? and (2) what are the main issues (problems) that users have reported about the apps?MethodsWe assess the popularity of the apps and end users’ perceptions based on user reviews in app stores. We conduct three types of analysis (data mining, sentiment analysis and topic modelling) to derive insights from the combined set of 25 583 user reviews of the aforementioned three apps (submitted by users until the end of 2020).ResultsResults show that end users have been generally dissatisfied with the apps under study, except the Scottish app. Some of the major issues that users have reported are high battery drainage and doubts on whether apps are really working.DiscussionTowards the end of 2020, the much-awaited COVID-19 vaccines started to be available, but still, analysing the users’ feedback and technical issues of these apps, in retrospective, is valuable to learn the right lessons to be ready for similar circumstances in future.ConclusionOur results show that more work is needed by the stakeholders behind the apps (eg, apps’ software engineering teams, public-health experts and decision makers) to improve the software quality and, as a result, the public adoption of these apps. For example, they should be designed to be as simple as possible to operate (need for usability).

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Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

Journal of Intelligent Systems ◽

10.1515/jisys-2019-0197 ◽

2020 ◽

Vol 30 (1) ◽

pp. 192-208 ◽

Cited By ~ 1

Author(s):

Hamza Aldabbas ◽

Abdullah Bajahzar ◽

Meshrif Alruily ◽

Ali Adil Qureshi ◽

Rana M. Amir Latif ◽

...

Keyword(s):

Logistic Regression ◽

Language Processing ◽

Mobile Application ◽

Knowledge Engineering ◽

Machine Learning Algorithms ◽

Application Development ◽

User Reviews ◽

N Gram ◽

Logistic Regression Algorithm ◽

Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

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“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

Symmetry ◽

10.3390/sym13020251 ◽

2021 ◽

Vol 13 (2) ◽

pp. 251

Author(s):

Yan Yan ◽

Faguo Zhou ◽

Yifan Ge ◽

Cheng Liu ◽

Jingwu Feng

Keyword(s):

Random Field ◽

Mobile Applications ◽

Conditional Random Field ◽

User Reviews ◽

Visualization System ◽

Target Extraction

With the spread of mobile applications and online interactive platforms, the number of user reviews are increasing explosively and becoming one of the most important ways for users to voice opinions. Opinion target extraction and opinion word extraction are two key procedures used to determine the helpfulness of reviews. In this paper, we implement a system to extract “opinion target:opinion word” pairs based on the Conditional Random Field (CRF). Firstly, we used the CRF model to extract opinion targets and opinion words, then combined these into pairs in order. In addition, Node.js was used to build a visualization system to display “opinion target:opinion word” pairs. In order to verify the effectiveness of the system, experiments were conducted on the Laptop and Restaurant datasets of SemEval-2014-task4, and the accuracy of the F value extracted by the model reached 86% and 90%, respectively. All the code and datasets for this experiment are available on GitHub.

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Explaining Review-Based Recommendations: Effects of Profile Transparency, Presentation Style and User Characteristics

i-com ◽

10.1515/icom-2020-0021 ◽

2020 ◽

Vol 19 (3) ◽

pp. 181-200

Author(s):

Diana C. Hernandez-Bocanegra ◽

Jürgen Ziegler

Keyword(s):

Decision Making ◽

Individual Differences ◽

User Interface ◽

Recommender Systems ◽

Social Awareness ◽

User Characteristics ◽

Presentation Style ◽

User Reviews ◽

Decision Making Styles

Abstract Providing explanations based on user reviews in recommender systems (RS) may increase users’ perception of transparency or effectiveness. However, little is known about how these explanations should be presented to users, or which types of user interface components should be included in explanations, in order to increase both their comprehensibility and acceptance. To investigate such matters, we conducted two experiments and evaluated the differences in users’ perception when providing information about their own profiles, in addition to a summarized view on the opinions of other customers about the recommended hotel. Additionally, we also aimed to test the effect of different display styles (bar chart and table) on the perception of review-based explanations for recommended hotels, as well as how useful users find different explanatory interface components. Our results suggest that the perception of an RS and its explanations given profile transparency and different presentation styles, may vary depending on individual differences on user characteristics, such as decision-making styles, social awareness, or visualization familiarity.

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