Generating Functional Requirements Based on Classification of Mobile Application User Reviews

Google play store allow the user to download a mobile application (app) and user get inspired by the rating and reviews of the mobile app. A recent study analyzes that user preferences, user opinion for improvement, user sentiment about particular feature and detail with descriptions of experiences are very useful for an application developer. However, many application reviews are very large and difficult to process manually. Star rating is given of the whole application and the developer cannot analyze the single feature. In this research, we have scrapped 282,231 user reviews through different data scraping techniques. We have applied the text classification on these user reviews. We have applied different algorithms and find the precision, accuracy, F1 score and recall. In evaluated results, we have to also find the best algorithm.

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Classification of Google Play Store Application Reviews Using Machine Learning

10.20944/preprints202003.0231.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Abdul Karim ◽

SAMIR BRAHIM BELHAOUARI ◽

Azhari SN ◽

Ali Adil Qureshi

Keyword(s):

Machine Learning ◽

Text Classification ◽

Mobile Application ◽

Mobile App ◽

User Preferences ◽

User Reviews ◽

Star Rating ◽

Single Feature ◽

Google Play

Google play store allow the user to download a mobile application (app) and user get inspired by the rating and reviews of the mobile app. A recent study analyzes that user preferences, user opinion for improvement, user sentiment about particular feature and detail with descriptions of experiences are very useful for an application developer. However, many application reviews are very large and difficult to process manually. Star rating is given of the whole application and the developer cannot analyze the single feature. In this research, we have scrapped 282,231 user reviews through different data scraping techniques. We have applied the text classification on these user reviews. We have applied different algorithms and find the precision, accuracy, F1 score and recall. In evaluated results, we have to find the best algorithm.

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Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

Egyptian Informatics Journal ◽

10.1016/j.eij.2021.03.001 ◽

2021 ◽

Author(s):

Asad Khattak ◽

Muhammad Zubair Asghar ◽

Zain Ishaq ◽

Waqas Haider Bangyal ◽

Ibrahim A Hameed

Keyword(s):

Sentiment Analysis ◽

User Reviews ◽

Analysis System

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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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PreLectO: An App for Cognitive Stimulation through Games in Early Childhood

Engineering Proceedings ◽

10.3390/engproc2021007046 ◽

2021 ◽

Vol 7 (1) ◽

pp. 46

Author(s):

Pedro Nogueiras ◽

Paula M. Castro ◽

Adriana Dapena

Keyword(s):

Learning Disabilities ◽

Early Childhood ◽

Training Program ◽

Cognitive Skills ◽

Mobile Application ◽

Cognitive Stimulation ◽

Functional Requirements ◽

Reading And Writing ◽

Common Learning ◽

Validation Tests

The goal of this work was to develop a mobile application for Android devices, with the objective of stimulating the cognitive skills of children from 0 to 6 years old who are suffering from learning disabilities, while focusing on the most common learning impediments such as reading and writing disorders. This application is based on games specifically designed to meet the needs of this group. For this purpose, we collaborated with professionals from an organization in the area of A Coruña who established the functional requirements of the application and carried out the validation tests. The application monitored the progress of its users, thus allowing the therapists to track them and adapt the training program to each of their individual needs.

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