Emotion and sentiment analysis for intelligent customer service conversation using a multi-task ensemble framework

Understanding users' exact feelings and enhancing enterprise customer relationship management depend heavily on emotion and sentiment analysis in intelligent customer service conversations. However, the research that is currently available analyzes either emotion or sentiment. This paper proposes a multi-task ensemble model that can perform multiple tasks of emotion and sentiment analysis simultaneously. This ensemble model via a multi-layer perceptron (MLP) network develops three deep learning models based on convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) for predicting intelligent customer service dialogue emotion and sentiment analysis, including "emotion classification and intensity", "valence and arousal for emotion", "valence and arousal for sentiment", and "3-class categorical classification for sentiment". The underlying problems cover two granularity analysis (i.e., coarse-grained and fine-grained) in the intelligent customer service domain. Experimental results suggest that the proposed multi-task ensemble model outperforms the single-task framework, and the method performs well in emotion and sentiment analysis tasks in intelligent service conversation.