Emotion analyzing system and method

Abstract

The invention discloses an emotion analyzing system and an emotion analyzing method. The system comprises a language database establishing module, a data preprocessing module, a perspective sentence identifying module and an emotion tendency analyzing module, wherein the language database establishing module is used for establishing a training set needed by perspective sentence identification and emotion tendency analysis; the data preprocessing module is used for preprocessing sentences in the training set; the perspective sentence identifying module is used for performing perspective sentence identification on the preprocessed sentences by adopting a support vector machine classifier and a Bayes classifier respectively, and integrally processing results of the classifiers to obtain a final classifying result; and the emotion tendency analyzing module is used for directly classifying the preprocessed sentences into positive, negative and non-viewpoint sentences respectively on the basis of the support vector machine classifier and the Bayers classifier, and integrating the classifying results of the vector machine classifier and the Bayers classifier through an integration formula to obtain a classifying result of a current sentence. Due to the adoption of the emotion analyzing system and the emotion analyzing method, the viewpoint sentence judging and emotion tendency classifying properties of Chinese microblogs can be improved.

Description

technical field [0001] The invention relates to a sentiment analysis system and method, in particular to a Chinese microblog sentiment analysis system and method. Background technique [0002] With the development of the Internet, more and more users express their opinions through the network platform, thus generating a large amount of subjective text data. The large amount of emotional information contained in these data has great potential value, and has broad development prospects in many fields such as social public opinion analysis, harmful information filtering, and product recommendation. However, the subjective sentiment classification of these data cannot be obtained through traditional keyword-based and automatic index information, and manual browsing of a large amount of text is very inefficient. In recent years, sentiment analysis for text has become a research hotspot, and related technologies have been applied in many fields such as electronic products, film a...

AI Technical Summary

Problems solved by technology

Although this method improves the performance of sentiment classification on Twitter, the accuracy of its subjective and objective classifiers is not very high (68.2% in the best case)

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