Aiming the spread of negative network public opinions in emergencies， a research method of emotional map of emergency based on sentiment analysis and influence evaluation was proposed. In the proposed method， a sentiment analysis model based on multi-head self-attention mechanism and Bi-directional Long Short-Term Memory network （Bi-LSTM） was proposed to evaluate website users’ emotional tendencies. Meanwhile， a point influence evaluation algorithm combining weighted degree and K-shell value was proposed to measure users’ influences. Based on the above models， the emotional map of emergency was constructed， which effectively improved the accuracy and scientificity of the emotional map. Taking “7.7 Anshun Bus Falling into Lake Incident” as an example， the life cycle of an emergency was divided into four stages such as outbreak stage， spread stage， maturity stage and decline stage， which were used to separately generate the emotional maps for visualization analysis. Experimental results show that， the F1-score of the proposed sentiment analysis model on the hotel review dataset is 9.92 percentage points and 2.5 percentage points higher than that of Recurrent Neural Networks for Text Classification （Text-RNN） model in positive and negative aspects respectively. On the Karate network， the discrimination and accuracy of the proposed influence evaluation algorithm are 46.89 percentage points and 29.05 percentage points higher than those of the K-shell algorithm respectively. By building the emotional map based on social networks， relevant department can find the opinion leaders and their tendencies， thereby grasping the development trend of online public opinion， and reducing the influence of negative emotions on society.