To address the issues that the sentiments of review texts in food safety network public opinions are various and depend on the topic and blog information， a Multi-Sentiment analysis model for review texts integrating topics and blogs named TBR-MSAM （Topic Blog Review-Multi-Sentiment Analysis Model） was proposed. Firstly， a Topic Blog Review-Feature Extraction （TBR-FE） module was constructed by using RoBERTa （Robustly optimized BERT （Bidirectional Encoder Representations from Transformers）^{pretraining approach） and deep learning models， and was used to extract contextual features of topic， blog and review information respectively. Secondly， a Topic Blog Review-Interactive Attention Feature Fusion （TBR-IAFF） module was built to conduct pairwise interactions between topic-review and blog-review to obtain interaction features and allocate weights reasonably， thereby exploring the complex relationships among topics， blogs and reviews. Thirdly， a Topic Blog Review-Cross Feature Fusion （TBR-CFF） module was constructed to conduct in-depth feature fusion on multiple pieces of information， thereby exploring users’ potential sentimental features. Finally， Softmax was used to classify the four sentiment polarities of review texts in food safety network public opinions. Experimental results on three constructed food safety network public opinion datasets show that compared to the optimal baseline model without topic and blog information， TBR-MSAM achieves at least 5.0 and 5.8 percentage points improvements in Macro-F1 and accuracy， respectively； compared to the optimal baseline model with topic and blog information， TBR-MSAM achieves the Macro-F1 and accuracy enhanced by at least 0.2 and 1.1 percentage points， respectively； compared to the optimal baseline model with topic， blog， and review text information， TBR-MSAM achieves the Macro-F1 and accuracy increased by at least 11.7 and 10.0 percentage points， respectively. The above verifies the effectiveness of TBR-MSAM in multi-sentiment classification task for food safety network public opinion.}