Micro blog text data classification method on basis of multi-feature fusion

Abstract

The invention discloses a micro blog text data classification method on the basis of multi-feature fusion, which comprises the steps: S1, carrying out user authentication login, acquiring micro blog text data information, acquiring feature information according to a classifier and calculating a weight of the feature information; S2, classifying the feature information by a naive Bayesian method and carrying out weight calculation on classification results; S3, carrying out classification fusion on the classification results of which calculation is completed until classification of the feature information is completed, and carrying out display operation on the feature information of which classification is completed. Efficiency that a user obtains useful information is improved, so that rapid capture of data is implemented and accuracy of data classification is ensured.

Description

technical field [0001] The invention relates to the field of computers, in particular to a method for classifying microblog text data based on multi-feature fusion. Background technique [0002] The purpose of sentiment analysis is to mine the opinions expressed by users and the emotional polarity from the text. Weibo is a new social network platform, which has the characteristics of diversity, real-time, short text and so on. Emotion recognition for Chinese microblogging has good application prospects in the fields of network public opinion analysis, information filtering, and user interest discovery. [0003] There are two types of existing sentiment classification techniques: machine learning methods and semantic methods. Pang et al. used machine learning methods for text sentiment classification, and compared the classification effects of naive Bayesian, maximum entropy models, and support vector machine methods. In addition, studies using word frequency, binary and n...

AI Technical Summary

Problems solved by technology

Therefore, there is still a big difference between the Chinese Weibo emotion recognition method and the traditional text

At the same time, due to the poor classification and extraction of Weibo emotional words, it has caused great confusion to users during use and reduced the efficiency of user-related data extraction.

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