Study of Sentiment on Google Play Store Applications

Abstract

This paper proposes an approach to perform sentiment analysis on Google play store’s applications considering Kaggle dataset. The dataset includes 10,000 applications with their ratings, reviews, number of download and other application related parameters. The dataset is pre-processed using a number of data cleaning steps, including data reduction, tokenization, stemming etc. For classification of sentiment polarity, a logistic regression model has been proposed. The logistic regression model has been extended to a tri-polar classifier. Accuracy of 81.1% was achieved for document-level classification of reviews. The top two applications, ‘Candy Crush Saga’ and ‘Clash of Clans’, have been selected for the analysis based on popularity. Even though the ‘family’ category has a higher number of applications on the store, games are much more popular on Google's play store. Results show that these apps, though popular, receive a higher number of negative reviews. The sentiment of play store users gives us an idea of how the applications market reacts, what are the needs and how to succeed in the android market. The opinion of the audience is an essential component for every business. These reviews prove to be useful for further development and improvement of the applications.