Emotion estimation from conversational sentences by autoregressive model

Abstract

With the rise in popularity of artificial intelligence, the technology of verbal communication between man and machine has received an increasing amount of attention, but generating a good conversation remains a difficult task. The key factor in human-machine conversation is Whether the machine can give consistent emotional expression as a response. we propose a model to estimate emotion by considering time series by autoregressive model. This model extracts emotional values from sentences and makes consistent emotion estimation by time series processing of emotional values. We perform a comparison between our models and comparative models and find that we can get a slightly better result with respect to emotional consistency. Problems also exist in emotion estimation at the current research stage, but time series processing proved to be effective in emotion estimation.