Text sentiment analysis has gradually become an important part of Natural Language Processing（NLP） in the fields of systematic recommendation and acquisition of user sentiment information， as well as public opinion reference for the government and enterprises. The methods in the field of sentiment analysis were compared and summarized by literature research. Firstly， literature investigation was carried out on the methods of sentiment analysis from the dimensions of time and method. Then， the main methods and application scenarios of sentiment analysis were summarized and compared. Finally， the advantages and disadvantages of each method were analyzed. According to the analysis results， in the face of different task scenarios， there are mainly three sentiment analysis methods： sentiment analysis based on emotion dictionary， sentiment analysis based on machine learning and sentiment analysis based on deep learning. The method based on multi-strategy mixture has become the trend of improvement. Literature investigation shows that there is still room for improvement in the techniques and methods of text sentiment analysis， and it has a large market and development prospects in e-commerce， psychotherapy and public opinion monitoring.

To solve the problems existing in BLP (Bell-LaPadula) model, such as information aggregation leakage, excessive privileges of trusted subject and the deficiency of integrity, with reference to the application requirement of hierarchical file protection, an information aggregation leakage proof model named IALP (Information Aggregation Leakage Proof) was proposed based on assignment partition. First of all, the cause of information aggregation leakage and the current research situation were discussed. Secondly, on the basis of assignments partition, the knowledgeable degree of subject and the information weight of object were quantized, and the relatively trusted subject was proposed. Security axioms and state transition rules were given. Finally, the theoretical proof, application examples and analysis indicate that IALP can control the knowable degree of the subject towards the object set with the aggregation leakage relation, and limits the privilege of trusted subject and enhances the integrity to some extent.