A Sentiment Analysis Method for Microblog Topics Driven by Social Relationships

Abstract

The invention relates to a microblog topic sentiment analysis method driven by the social relation. The method includes the following steps of firstly, conducting microblog text segmentation and stop word removal preprocessing on a microblog message set, extracting microblog user relation distribution G, and setting a sentiment dictionary; secondly, conducting sentiment polarity and topic belonging priori processing on microblog messages through the sentiment dictionary; thirdly, initiating the distribution parameters of a social relation topic sentiment model (SRTSM) and making circulating control counters C1 and C2 return to zero; fourthly, conducting iterative updating on a variable VarSet (please see the formula in the description) continuously through the SRTSM; fifthly, judging and outputting the sentiment polarity of a microblog m. By means of the method, the topic sentiment mode hidden in the microblog messages can be effectively found, and the microblog sentiment classification accuracy is 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 driven by social relations applied in the Web2.0 environment. Background technique [0002] Weibo is an integrated and open Internet social service emerging in the Web 2.0 era, which enables users to post short text messages to the public. Due to its simplicity, it is increasingly favored by Internet users. At present, the number of Sina Weibo users has exceeded 300 million, and a large number of Weibo messages are released every day. Among these massive microblog messages, there are many resources full of personal emotions. How to efficiently and automatically extract topics and emotions from these microblog messages is a hotspot with great research value. [0003] As a social platform, Weibo users have social relationships such as attention, fans, and mutual attention among users. Users who follow each other ...

AI Technical Summary

Problems solved by technology

[0008] We can see that the emotional polarity of Weibo 2 is also positive, but the existing LDA topic sentiment model may classify the emotional polarity of Weibo 2 as negative emotional polarity, because it does not consider user U1 and user U2 The relationship between U1 and U2, because U1 and U2 pay attention to each other, can indicate to a certain extent that they have similar personalities and similar interests. The overall emotional polarity of user U1’s Weibo is positive. When judging the Weibo posted by user U2, it should be It is believed that user U2’s Weibo has a higher probability of positive sentiment polarity, and the positive sentiment polarity parameter should be larger than the negative sentiment polarity parameter. However, the existing LDA topic sentiment model assumes that the texts are independent of each other, so this effect cannot be achieved.

[0009] From the above analysis, we can see that the existing representative LDA topic sentiment model does not consider the relationship between Weibo users well, which may lead to a decrease in the accuracy of Weibo sentiment analysis.

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