Sensitive Information Topics-Based Sentiment Analysis Method for Big Data

With the rapid development of the Internet, more and more users expressed their views on the Internet. Therefore, the big data of texts are generated on the Internet. In the era of big data, mining the sentiment tendencies contained in massive texts on the Internet through natural language processing technology has become an important way of public opinion supervision. In this paper, the sensitive information topics-based sentiment analysis method for big data is proposed. This method integrates topic semantic information into text representation through a neural network model. The attention mechanism is introduced into the neural network, and context-aware vector is introduced to calculate the weight of each word. In addition, in order to make the model more adaptable, the method of sentiment dictionary tagging is used to obtain the training data. The experimental results show that the proposed model can effectively improve the accuracy of sentiment analysis results.