Method and system for establishing text information analysis model integrated with multiple element analysis

Abstract

The invention discloses a method and system for establishing a text information analysis model integrated with multiple element analysis, and the method comprises the steps: obtaining a training textinformation sample, training an emotion function through the training text information sample, wherein the emotion function calculates a corresponding emotion value of an inputted text; obtaining theinputted text, and inputting the inputted text into the sentiment function to obtain a sentiment value about the inputted text as a sentiment value; and selecting an analysis result satisfying the emotion value from a plurality of analysis results related to the input text as a semantic analysis result, and displaying the semantic analysis result. The emotion function for calculating the emotion value is obtained through function training, so that the corresponding emotion value can be calculated from the input text, the text analysis result is selected according to the emotion value, and thesemantics of the text can be better determined.

Description

technical field [0001] The invention relates to the technical field of Internet information content analysis, in particular to a method and system for establishing a text information analysis model that integrates analysis of various elements. Background technique [0002] Sentiment analysis (Sentiment analysis) is the process of analyzing, processing, summarizing and reasoning subjective texts with emotional color, the purpose is to extract user opinions from text information with subjective emotional color published by users, and judge their emotions polarity. [0003] Due to the complexity of human emotions, there is no uniform standard for the division of emotion categories. Common methods, for example, divide the emotion classification task into two types: binary classification of subjective and objective information, and emotional classification of subjective information, including the most common binary classification of praise and criticism and more detailed multiva...

AI Technical Summary

Problems solved by technology

[0004] For emotion monitoring methods, foreign methods have proposed a distance supervised learning method for emotional classification of messages in Twitter, that is, given a search term, messages are automatically classified into positive or negative information, and messages containing emoticons in Twitter are extracted as training sets , and finally use algorithms such as naive Bayesian, maximum entropy, and support vector machines for classification; for Chinese, a multi-strategy method based on hierarchical structure is proposed to conduct sentiment monitoring research on text data, and topic-related features are used in feature extraction , the analysis process of this method is more cumbersome

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