![]() To demonstrate the impact of proposed model, comprehensive experiments are performed on seven different datasets. ![]() On the other hand, word embedding models such as Word2Vec, GloVe, and FastText are employed for text representation. For this purpose, CNNs, RNNs, LSTMs, BERT, MBERT, DistilBERT, RoBERT are used for classification purpose. To our knowledge, this is the first study to evaluate loyalty of customers analyzing sentiments of users from their comments using deep learning, word embedding, and deep contextualized word representation models. In this work, we introduce sentiment analysis-based customer loyalty prediction in mobile applications using word embeddings, deep learning algorithms, and deep contextualized word representations. Most of the studies so far focus on churn prediction or customer loyalty in mobile applications by analyzing demographic, economic, and behavioral data about customers. Therefore, it is important to predict when players tend to leave an application. In mobile applications, it is observed that the demand rises with the usage of mobile devices such as smartphones. With over 600,000 samples demonstrate that the proposed MBST achieves the F-score of 82.72% and the Area Under Curve (AUC) of 93.75%, which significantly outperforms state-of-the-art methods in terms of churn prediction.Ĭustomer loyalty is important for many industries, including banking, telecommunications, gaming, and shopping, in terms of sustainability. Extensive experiments on a real-world Tencent QQ browser dataset Furthermore, a Tree-based classifier is attached for churn prediction instead of using the multilayer perceptron. ![]() To meet this challenge, we propose a novel model named Multivariate Behavior Sequence Transformer (MBST) with two complementary attention mechanisms to explore the temporal and behavioral information separately. However, traditional churn prediction algorithms such as Tree-based models cannot exploit the temporal characteristics of browser customers behaviors, while sequence models cannot explicitly extract the information between multiple behaviors. Churn prediction based on customer behaviors plays a vital role in customer retention strategies. In the competitive web browser market, identifying potential churners is critical to decreasing the loss of existing customers. ![]()
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