Sentiment Analysis Based Churn Prediction in Mobile Games using Word Embedding Models and Deep Learning Algorithms

Abstract

Customer churn is one of the most important problems for many industries, including banking, telecommunications, and gaming. In the gaming market, it is observed that the demand on game applications rises with the usage of mobile devices such as smartphones. Because of this, it is important to predict when players tend to leave a game. Studies so far focus on churn prediction in mobile or online games by analyzing demographic, economic, and behavioral data about their customers. In this work, we introduce a sentiment analysis-based churn prediction model in mobile games using word embedding models and deep learning algorithms. To the best of our knowledge, this is the first study to evaluate the churn tendency of customers by analyzing sentiments of players from their comments on games using deep learning and word embedding models. For this purpose, we use deep learning algorithms for classification and word embedding models for text representation. The applied deep learning algorithms include convolutional neural networks, recurrent neural networks, long short-term memory networks. Word2Vec, GloVe, and FastText word embedding models are employed for text representation. To demonstrate the impact of proposed model, comprehensive experiments are carried out on Turkish four different game datasets. The experiment results show that sentiment analysis of players in mobile games can be powerful indicator in terms of predicting customer churn.