Sentiment analysis is one of the hot research areas of information technology, which is most probably utilized in the industrial system such as Twitter, Facebook, etc. to create, deploy, and publish mobile apps on official marketplaces. The billion-dollar success of applications has enchanted many developers. As a result, mobile applications must maintain a high rating to be successful. Previous research on the predictability of mobile app success tried to identify significant factors that affect high ratings from app stores. This paper presents an automated method for predicting the sentiment of mobile app reviews using data from the Google Play Store platform. We scraped data from the Google Play Store app and then used Google Translator to translate it. After translation, we utilized the Urdu Hack 'Urdu Language' grammar dataset and then implemented National Language Pre-processing methods. Finally, we develop our emotional analysis model using machine learning approaches. We test our method and find that it has a prediction accuracy of 86% with the Logistic Regression Classifier. We present evidence that user interactions can impact the success of mobile applications based on these findings.