To address the problems that many existing studies ignore the correlation between interlocutors’ emotions and sentiments， a sentiment boosting model for emotion recognition in conversation text was proposed， namely Sentiment Boosting Graph Neural network （SBGN）. Firstly， themes and dialogue intent were integrated into the text， and the reconstructed text features were extracted by fine-tuning the pre-trained language model. Secondly， a symmetric learning structure for emotion analysis was given， with the reconstructed features fed into a Graph Neural Network （GNN） emotion analysis model and a Bi-directional Long Short-Term Memory （Bi-LSTM） sentiment classification model. Finally， by fusing emotion analysis and sentiment classification models， a new loss function was constructed with sentiment classification loss function as a penalty， and the optimal penalty factor was adjusted and obtained by learning. Experimental results on public dataset DailyDialog show that SBGN model improves 16.62 percentage points compared with Dialogue Graph Convolutional Network （DialogueGCN） model， and improves 14.81 percentage points compared with the state-of-art model Directed Acyclic Graph-Emotion Recognition from Conversation （DAG-ERC） in micro-average F1. It can be seen that SBGN model can effectively improve the performance of emotion analysis in dialogue system.