User Sentiment Analysis in Conversational Systems Based on Augmentation and Attention-based BiLSTM
Mourad Jbene, Mourad raif, Smail Tigani, Abdellah Chehri, Rachid Saadane
Abstract
Conversational Systems are increasingly substituting humans in many service industries. They aim to provide human-like interaction with users for task completion or chitchat in a conversation style. User sentiment analysis is an important task that can help better understand users’ behavior and satisfaction in conversations. Although some researchers have studied the problem of sentiment analysis, most of the existing methods are oriented toward general felds. To overcome the challenges of sentiment analysis, we propose a BE-Att-BiLSTM, which stands for an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model. The proposed model uses pre-trained BERT, contextual embeddings and a combination of BiLSTM and attention mechanism for efficient sentiment analysis in conversations. In addition, text-augmentation techniques are leveraged to enhance the performance of the proposed model. Experimental results on a public benchmark dataset show an improved accuracy of 68.00% and an F1-score of 67.50%.