With the development of information technology and social media

user sentiment analysis tends to play an increasingly important role in public opinion monitoring

information prediction and product evaluation. However

collecting sufficient manual sentiment labels in supervised learning is still difficult and costly

and unsupervised learning is lack of label guidance. Therefore

a semi-supervised sentiment analysis model based on sociological theory is established in this paper

which is mainly divided into two parts: label addition and emotion analysis. First

a UR-S (User Relationship using Social relations) model was built

which was inspired by sentiment consistency and emotional contagion. Then a TRS-SAT (Text Relationship Strength using Social relations

user Attribute and Text similarities) model based on UR-S model and add labels was established. Finally

the TRS-SAT model and CNN (convolutional neural network) were combined to construct SA-SRS-CNN (Sentiment Analysis using Social Relationship Strength and Convolutional Neural Network) model. The model uses CNN to mine the deep connection between the feature set and the emotional labels to improve the emotional performance. Experiments show that the accuracy

recall

and the F value of the proposed model increased by 11.40%

5.90% and 8.65%

respectively compared with SVM

and increased 4.12%

4.17%

and 4.14%

respectively compared with CNN

which suggests that the model is innovative and practical and can provide a good theoretical basis for public opinion analysis.