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Abstract
The Google Play Store is one of the world’s largest Android app marketplaces, hosting on the order of two million applications. To help developers interpret the vast volume of user feedback, we analyze a publicly available dataset of 9,146 Google Play apps (from Kaggle, covering dozens of categories). We conduct enhanced exploratory data analysis (EDA) of app metadata and then apply Natural Language Processing to user reviews to classify sentiment. Specifically, we train four supervised classifiers (Naïve Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbors) on the preprocessed review text. We evaluate each model using accuracy, precision, and recall. Our experiments show that logistic regression attains the highest accuracy (around 90%), with balanced precision and recall, while Random Forest and KNN perform comparably (≈87-89%) and Naïve Bayes trails behind (~59%). These updated results provide insights into app market trends: for example, Games and Communication apps dominate install totals, and over 97% of apps are free. Most user reviews are positive (as reported in prior app-review studies), reinforcing that sentiment analysis can help developers understand user preferences and improve app quality.
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