SEOVER: Sentence-Level Emotion Orientation Vector Based Conversation Emotion Recognition Model

Zaijing Li, Fengxiao Tang, Tieyu Sun, Yusen Zhu, Ming Zhao

Abstract: In this paper, we propose a new expression paradigm of sentence-level emotion orientation vector to model the potential correlation of emotions between sentence vectors. Based on it, we design an emotion recognition model referred to as SEOVER, which extracts the sentence-level emotion orientation vectors from the pre-trained language model and jointly learns from the dialogue sentiment analysis model and extracted sentence-level emotion orientation vectors to identify the speaker’s emotional orientation during the conversation. We conduct experiments on two benchmark datasets and compare them with the five baseline models. The experimental results show that our model has better performance on all data sets.