With the development of social media, users' evaluations have become a key factor in network decision-making.Owing to the necessity of making a more accurate analysis on the emotional tendency of social media users' evaluations as well as promoting public opinion analysis and recommendation algorithms, a sentiment analysis model called Bi-LSTMM-B (Bi-directional Long Short Term Memory Model with Maxout neurons in Bagging algorithm) is proposed.With the feature of combining deep learning model and the idea of ensemble learning, the model improves the Bi-LSTM model and the Bagging algorithm.On the one hand, the Bi-LSTMM model introduces the Maxout neural into the Bi-LSTM model to solve the vanishing gradient problem during the stochastic gradient descent training and optimize the training process.On the other hand, multiple emotional classifiers were trained at the foundation of the Bagging algorithm.The out of bag data assigns the weight for each classifier on specified category according to their performance.Hence the voting strategy is improved to enhance the generalization ability of the model.The experimental results indicate that the accuracy of the Bi-LSTMM-B model is improved by 12.08% compared to the traditional LSTM model.It is also superior to other contrast models in the recall rate and F value.Therein, the introduction of Maxout neurons has a relative improvement effect of 8.28% on the accuracy of sentiment analysis, while the improved voting strategy accounts for 4.06% on the accuracy.Thus, it proves that combining deep learning and ensemble learning contributes to the improvement of the accuracy of sentiment analysis, which shows some value in research.