Microblog theme emotion analysis method based on mixed characteristic calculation

Abstract

The invention relates to a microblog theme emotion analysis method based on mixed characteristic calculation. The method comprises the following steps of (1) preprocessing microblog data: extracting microblog characteristics by applying Chinese words segmentation, English stemming and emotion extraction technologies, and utilizing priori knowledge to carry out initial assignment on the emotion and the theme of the microblog characteristics; (2) initializing algorithm parameters; (3) utilizing a Gibbs sampling technology to carry out valuation on joint distributions A and H of the parameters of a multi-characteristic theme emotion model MfJST; and (4) judging emotion polarity of each microblog through the appearance probability of the emotion polarity in the microblog; and detecting an implied theme of a microblog message through carrying out marginalization about a microblog emotion variable on the joint distribution A. according to the method, the concealed theme concerned by a user and the concealed real emotion of the user in the microblog message can be effectively excavated, the method is applied to various social media, such as Twitter and Sina microblog, the online public opinion monitoring can be optimized, and the information service quality can be improved.

Description

technical field [0001] The invention relates to the technical field of network public opinion analysis, in particular to a microblog topic sentiment analysis method based on hybrid feature calculation applied to social networks under the Web2.0 environment. Background technique [0002] Web2.0, with the core spirit of free, open and sharing, makes users the protagonists of the Internet. Platforms such as social networking sites, microblogs and BBS forums provide economical and convenient channels for netizens to express their opinions and exchange emotions. Generally speaking, users’ comments on the Weibo platform are short but full of personal emotions and subjective tendencies. For example, different readers hold different views on the same news event, and different users have different opinions on a certain mobile phone. With its personalized user experience, different movie lovers will leave different viewing comments for the same movie, and so on. Research on how to ef...

AI Technical Summary

Problems solved by technology

Unfortunately, the existing unsupervised sentiment analysis methods are difficult to be directly used for microblog sentiment analysis, mainly because most of these methods ignore several basic facts to varying degrees: 1) The emotional polarity of microblog messages It is closely related to the theme of Weibo, and it is impossible to have emotions that are dissociated from the theme; 2) Weibo has the characteristics of short length, irregularity and multi-modality, and many data that are regarded as noise in traditional text mining It is particularly important in blog messages, such as ACSII artistic emoticons; 3) There are differences in the personalities of Weibo user groups, and Weibo users with different personalities express their thoughts and feelings in different ways, whether implicit or euphemistic or straightforward

The neglect of these fundamental facts leads to unsatisfactory sentiment analysis and topic detection for such unsupervised methods

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