  For the task of conversation emotion recognition, recent works focus on
speaker relationship modeling but ignore the role of utterance's emotional
tendency.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,
which extracts the sentence-level emotion orientation vectors from the 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.
