Predicting sentiment analysis for Web users with a deep learning approach

Abstract In the last decade Sentiment analysis has been an interesting research topic in natural languageprocessing (NLP) and data mining fields. We have noticed that deep neural network (DNN)models are always applied in sentiment analysis researches to obtain good results. There aremany neural architectures which are applied for sentiment analysis. Among these architectures,Long Short-Term Memory (LSTM) models are the most used ones. In this contribution, we collectedour dataset from our developed web application. The obtained dataset contains differenttypes of features; Quality of Service (QoS) metrics, Web Quality of Experience (QoE) metrics,user engagement metrics, comments to videos that are included in the application, and the MeanOpinion Score (MOS) expressed by users. After that, we trained our dataset on our proposedmodel LSTM-CNN-RNN to predict the MOS. As a conclusion, we obtained a good accuracyand a low loss rate.