scholarly journals Identification of Fraud Apps Using Sentiment Analysis Techniques

2021 ◽  
Vol 15 (23) ◽  
pp. 178-185
Author(s):  
Abeer Aljumah ◽  
Amjad Altuwijri ◽  
Thekra Alsuhaibani ◽  
Afef Selmi ◽  
Nada Alruhaily
Keyword(s):  
Sentiment Analysis ◽  
User Feedback ◽  
Sensitive Data ◽  
User Reviews ◽  
Analysis Techniques ◽  
Android App ◽  
Fake Reviews ◽  
Google Play

Considering that application’s security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to spot insecure apps by analyzing user feedback on Google Play platform using sentiment analysis. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application. But unfortunately, not all of these reviews are real, fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews and deliver accurate and correct results. This tool is useful for both users wanting to install an android app and for developers interested in app’s optimization.

scholarly journals Android Apps Security Assessment using Sentiment Analysis Techniques: Comparative Study

2021 ◽  
Vol 15 (24) ◽  
pp. 123-133
Author(s):  
Abeer Aljumah ◽  
Amjad Altuwijri ◽  
Thekra Alsuhaibani ◽  
Afef Selmi ◽  
Nada Alruhaily
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Security Assessment ◽  
Sensitive Data ◽  
User Reviews ◽  
Application Security ◽  
Analysis Techniques ◽  
Android Apps ◽  
Fake Reviews ◽  
Google Play

Considering that application security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to trying to spot insecurity apps, by analyzing user feedback on the Google Play platform and using sentiment analysis to determine the apps level of security. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application or not. But unfortunately, not all of these reviews are real, and as is known, the fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews to be the result is accurate and correct. This study is useful for both users wanting to install android apps and for developers interested in app optimization.

scholarly journals A first look at Android applications in Google Play related to COVID-19

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Jordan Samhi ◽  
Kevin Allix ◽  
Tegawendé F. Bissyandé ◽  
Jacques Klein
Keyword(s):  
Mobile Apps ◽  
Contact Tracing ◽  
Location Tracking ◽  
Response Effort ◽  
Sensitive Data ◽  
Android Apps ◽  
Digital World ◽  
Public Health Organizations ◽  
Android Applications ◽  
Google Play

AbstractDue to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the COVID-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a first taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the COVID-19, with dedicated apps released as early as January 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations.

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.

A review of sentiment analysis techniques for opinionated web text

2016 ◽  
Vol 4 (2-4) ◽  
pp. 241-247 ◽  
Author(s):  
Jaspreet Singh ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Sentiment Analysis ◽  
Analysis Techniques

scholarly journals A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 462
Author(s):  
G Krishna Chaitanya ◽  
Dinesh Reddy Meka ◽  
Vakalapudi Surya Vamsi ◽  
M V S Ravi Karthik
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Buying Behavior ◽  
Online Retail ◽  
User Reviews ◽  
Business Plans ◽  
Learning Techniques ◽  
User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general. 

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