Classification of Mobile Application User Reviews for Generating Tickets on Issue Tracking System

2019 ◽  
Author(s):  
Kittisak Phetrungnapha ◽  
Twittie Senivongse
Keyword(s):  
Mobile Application ◽  
Tracking System ◽  
User Reviews ◽  
Issue Tracking System

scholarly journals Classification of Google Play Store Application Reviews Using Machine Learning

2020 ◽  
Author(s):  
Abdul Karim ◽  
Azhari Azhari ◽  
Meshrif Alruily ◽  
Hamza Aldabbas ◽  
Samir Brahim Belhaouri ◽  
...  
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 also find the best algorithm.

Classification of Google Play Store Application Reviews Using Machine Learning

2020 ◽  
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.

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

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

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

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
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.

scholarly journals Mobile Application for Detecting Vehicle Accident and Tracking System using GPS/GSM

IJARCCE ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 298-305
Author(s):  
Mr. Sanjay Chavan ◽  
Miss. Ankita Shendkar ◽  
Prof. Avinash Palave
Keyword(s):  
Mobile Application ◽  
Tracking System ◽  
Vehicle Accident

scholarly journals Design a Mobile Application for Children’s Tracking in Crowded Environments

2021 ◽  
Author(s):  
Tanweer Alam ◽  
Abdirahman Ahmed Hadi ◽  
Rayyan Qari Shahabuddin Najam ◽  
Shamimul Qamar
Keyword(s):  
Elderly People ◽  
Mobile Application ◽  
Tracking System ◽  
The Elderly ◽  
The Other ◽  
Disabled People ◽  
The Disabled ◽  
To Come ◽  
Children First ◽  
Crowded Environments

Child Tracking System is a mobile application where the parent can monitor their children location in crowded environments. In addition to children, there is also the elderly people, and the disabled people, so the guidance or the person responsible of them can use this application to track their location. The parent or guidance side will have the application in which they can track, and on the other side, the child or the old person or the disabled person will have device that includes the GPS chip. The main goal of this research is to design an application with system that will help parents to keep track of their children, eventually reducing the cases in which the children or the other mentioned categories of people could be lost. The current used solution to this problem is that the children first have a wearable hand wrist in which they print their parent phone number, so when the child is lost there is a center in which the child is being taken and dealt with care till they contact the parent to come and pick the child up. The problem with the current way that it takes time, and there is a risk that child get totally lost or kidnapped before even reaching to any help, so the new way is better to even prevent them to go far away or to be lost for hours, thus the recovery here will be fast unlike the regular used way nowadays. That goal will be achieved throw systematically objectives starting from studying the existed systems, to planning and analysing, going to designing and implementing, and lastly, testing our own system.

scholarly journals Partially observable Markov Decision Process to prioritize software defects

2021 ◽  
Author(s):  
Shirin Akbarinasaji
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Tracking System ◽  
Dependency Graph ◽  
Relative Importance ◽  
Bug Reports ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
Partially Observable ◽  
Issue Tracking System

Background: Bug tracking systems receive many bug reports daily. Although the software quality team aims to identify and resolve these bugs, they are never able to fix all of the reported bugs in the issue tracking system before the release deadline. However, postponing the bug fixing may have some consequences. Prioritization of bug reports will help the software manager decide which bugs to fix and which bugs to postpone. Typically, bug reports are prioritized based on the severity, priority, time and effort for fixing, customer pressure, etc. Aim: Previous studies have shown that these factors may not be appropriate for prioritization. Therefore, relying on them to automate bug prioritization might be misleading. In this dissertation, we aim to prioritize bug reports with respect to the consequence of not fixing the bugs in terms of their relative importance in the issue tracking system. Method: In order to measure the relative importance of bugs in the issue tracking system, we propose the construction of a dependency graph based on the reported dependency-blocking information in the issue tracking system. Two metrics, namely depth and degree, are used to measure the relative importance of the bugs. However, there is uncertainty in the dependency graph structure as the dependency information is discovered manually and gradually. Owing to this uncertainty, prioritization of bugs in the descending order of depth and degree may be misleading. To handle the uncertainty, we propose a novel approach of a partially observable Markov decision process (POMDP) and partially observable Monte Carlo planning (POMCP). Result: To check the feasibility of the proposed approach, we analyzed seven years of data from an open source project, Firefox, and a commercial project. We compared the proposed policy with the developer policy, maximum policy, and random policy. Conclusion: The results suggest that software practitioners do not consider the relative importance of bugs in their current practice. The proposed framework can be combined with practitioners’ expertise to prioritize bugs more effectively and take the depth and degree of bugs into account. In practice, the POMDP framework with the POMCP planner can help practitioners sequentially select bugs to minimize the connectivity of the dependency graph.

scholarly journals Automatic Target Acquisition and Bull Tracker using Gps and Gsm Technology

2020 ◽  
Vol 9 (3) ◽  
pp. 2776-2779
Keyword(s):  
Real Time ◽  
Mobile Application ◽  
Tracking System ◽  
Target Acquisition ◽  
Time Intervals ◽  
Regular Time ◽  
Arduino Microcontroller

An efficient bull tracking system is designed and implemented for tracking the movement of any bull from any location at any time. The designed device works using GPS and GSM technology for bull tracking. Arduino microcontroller is used to control the GPS and GSM module. The device is embedded on a bull whose position is to be determined and tracked in real time. The microcontroller is used to control the GPS module to get the coordinates at regular time intervals. The GSM module is used to transmit the updated coordinates of bull location to the client via SMS and mobile application. When the SMS is received, the app will automatically read the SMS and update the location of the bull to the user. This device will help the user to always keep an eye on their bull.

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