In this research, the ratings and users’ attitudes of the apps on Google play store are analyzed in view of developing a pattern or cause that may define app success. By extracting useful information from a large number of app reviews, the goal of our study is to provide useful and actionable recommendations for developers and marketers of the apps.
Our methodology involves:
- Data Collection: Collecting a set of application ratings and the customers’ reviews collected from Google Play Store..
- Data Pre-processing: From data pre-processing, data cleaning entails handling of missing values as well as grounding of textual data to fit sentiment analysis.
- Sentiment Analysis: Using the NLP approaches for performing sentiment analysis for the evaluation of user sentiments in the app review.
- Comparative Analysis: The comparison of free and paid apps based on their sentiment polarity.
- Data Visualisation: Utilising visual representations to analyse the distribution of sentiment polarity and app ratings in order to identify noteworthy patterns.
The findings indicate that there is a notable disparity in user sentiments between free and paid applications, with paid applications generally garnering higher sentiment scores. This analysis offers valuable insights for app developers to improve user experience and for marketers to plan app promotions strategically. The study highlights the significance of user feedback in the process of app development and marketing
