Comparative Sentiment Analysis of App Reviews

Abstract

Google app market captures the school of thought of users via ratings and text reviews. The critique's viewpoint regarding an app is proportional to their satisfaction level. Consequently, this helps other users to gain insights before downloading or purchasing the apps. The potential information from the reviews can't be extracted manually, due to its exponential growth. Sentiment analysis, by machine learning algorithms employing NLP, is used to explicitly uncover and interpret the emotions. This study aims to perform the sentiment classification of the app reviews and identify the university students' behaviour towards the app market. We applied machine learning algorithms using TF-IDF text representation scheme and the performance was evaluated on ensemble learning method. Our model was trained on Google reviews and tested on students' reviews. SVM recorded the maximum accuracy(93.37%), Fscore(0.88) on tri-gram + TF-IDF scheme. Bagging enhanced the performance of LR and NB with accuracy of 87.80% and 85.5% respectively.