On a daily basis, there are millions of applications that are being uploaded by the developers. Millions of users download these applications without checking for duplicates. These applications hit the personal information of the users, which damages the users’ trust in the Google Store and also causes a loss for the users. Aggregately, there is a little information about these applications, which is given on the Play Store. In this research work, we have scrapped a Google Play Store dataset with the help of a crawler or scraper that is used to index, measure, and analyze the millions of Play Store applications. After retrieving the information about the applications from the Play Store, we have different categories of applications. These are paid and free applications, and we have a list of different categories of home applications and game applications. In this research, we have analyzed the applications’ content by exploring different research questions on the basis of the attributes of a dataset. We have checked the causal structures from datasets, associations, correlation, frequent patterns, and sentiment analysis of user reviews by using the different machine learning techniques that will be beneficial for developers and users. Using latent semantic analysis, which is a text mining approach, we have analyzed user reviews on game applications. We find that the determinants that create either user positiveness or negativity toward game applications. We have analyzed the correlation between the average sentiment analysis and the average rating of the applications. We have evaluated that most of the applications that show the top ranking in the rating graph are not correct in the review sentiment analysis graph.