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.

Most of the existing drowsiness recognition algorithms are based on machine learning or deep learning， without considering the relationship between the sequence of human eye closed state and drowsiness. In order to solve the problem， a drowsiness recognition algorithm based on human eye state was proposed. Firstly， a human eye segmentation and area calculation model was proposed. Based on 68 feature points of the face， the eye area was segmented according to the extremely large polygon formed by the feature points of human eye， and the total number of eye pixels was used to represent the size of the eye area. Secondly， the area of the human eye in the maximum state was calculated， and the key frame selection algorithm was used to select 4 frames representing the eye opening state the most， and the eye opening threshold was calculated based on the areas of human eye in these 4 frames and in the maximum state. Therefore， the eye closure degree score model was constructed to determine the closed state of the human eye. Finally， according the eye closure degree score sequence of the input video， a drowsiness recognition model was constructed based on continuous multi-frame sequence analysis. The drowsiness state recognition was conducted on the two commonly used international datasets such as Yawning Detection Dataset （YawDD） and NTHU-DDD dataset.Experimental results show that， the recognition accuracy of the proposed algorithm is more than 80% on the two datasets， especially on the YawDD， the proposed algorithm has the recognition accuracy above 94%. The proposed algorithm can be applied to driver status detection during driving， learner status analysis in class and so on.