Dynamic emotion modelling and anomaly detection in conversation based on emotional transition tensor

Conversational data in social media contain a great deal of useful information, and conversation anomaly detection is an important research direction in the field of sentiment analysis. Each user has his or her own specific emotional characteristic, and by studying the distribution and sampling the users' emotional transitions, we can simulate specific emotional transitions in the conversations. Anomaly detection in conversation data refers to detecting users' abnormal opinions and sentiment patterns as well as special temporal aspects of such patterns. This paper proposes a hybrid model that combines the convolutional neural network long short-term memory (CNN-LSTM) with a Markov chain Monte Carlo (MCMC) method to identify users' emotions, sample users' emotional transition and detect anomalies according to the transition tensor. The emotional transition sampling is implemented by improving the MCMC algorithm and the anomalies are detected by calculating the similarity between the normal transition tensor and the current transition tensor of the user. The experiment was carried on four corpora, and the results show that emotions can be well sampled to conform to user's characteristics and anomaly can be detected by the proposed method. The model proposed can be used in intelligent conversation systems, such as simulating the emotional transition and detecting the abnormal emotions.