Predicting Mobile Application Breakout Using Sentiment Analysis Of Facebook Posts

Publishing mobile applications on the official stores is becoming a big business. Many developers are charmed by the billion-dollar success of breakout applications. Thus, in order to ensure success, mobile applications need to sustain top ranking. Previous work on the predictability of mobile applications success aimed to extract from app stores relevant features that influence high rating. In this article, we propose an automated approach to exploit data available on Facebook platform that predicts mobile applications breakout. We collect data from Facebook graph API, then determine sentiment polarity of user comments. We design statistical features to score users sentiment for each post. Then, we compose posts scores with Facebook statistical measures to form a mobile applications breakout dataset. Finally, we use machine learning techniques to build our breakout prediction model. We evaluate our approach with 199 mobile applications and obtain a prediction accuracy of 83.78%. We find that Likes count on a Facebook page is decisive for climbing mobile applications ranking. However, a high rate of negative opinions declines application ranking and deprives mobile application of achieving a breakout. Based on these findings, we provide evidence that user interactions on social networks can influence the success of mobile applications.