Determining of The User Attitudes on Mobile Security Programs with Machine Learning Methods
Security plays an important role in today's virtual world. Cybersecurity software has widely been used by the development of portable virtual environments. Smartphones take place in an important part of our lives. Daily routines are carried out over mobile phones, especially after the Covid-19 pandemic process. Due to its ease of use, compulsory or optional mobile phone use brought also about a lot of security concerns. Mobile security software is used for different purposes such as virus removal and protection of personal information according to user preferences. In the field of natural language processing, user preferences can now be analyzed on the basis of machine learning methods with sentiment analysis. In this paper, the preference reasons for mobile security software are analyzed with machine learning methods based on user comments and sentiment analysis. In the study, all user comments were classified into 10 main categories and the user preferences of mobile security programs were analyzed.
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