Sentiment analysis on google play store app users’ reviews based on deep learning approach

Most people are tending to download or purchase apps, as a result of globally spreading technologies and smartphone usability. The Google Play Store app market is one of the most famous and rapidly increasing app markets and it captured the users’ thoughts, feelings, and, opinions about the appropriate apps they used. It is helpful for new users, app developers, and app creators to gain insights into the existing audience's opinion about relevant apps. Therefore, this study mainly aims to perform sentiment analysis on Google Play Store app users’ reviews based on the 15 latest apps. We collected 33,000 user reviews and implemented a machine-learning algorithms after initially pre-processing the data and extracting features through Term Frequency—Inverse Document Frequency (TF-IDF) vectorizer tool. The Artificial Neutral Network (ANN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) algorithms were used for the comparison of results. By applying these algorithms, users’ reviews are mainly categorized into neutral, positive, and negative in each app separately. The overall results show that LSTM outperformed both ANN, and SVM and had a greater accuracy, recall, f-measure, and lowest error rates across all apps for providing valuable insights for sentiment classification. According to the outcomes, LSTM produces the best sentiment analysis outcomes for keeping track of users' app reviews. Affirming 80–90% accuracy in each Apps, the outcomes reinforce the all model's validity in understanding user attitudes, consolidating the method's effectiveness. The results useful for new app users and app developers to taking their decisions.