A microblog topic sentiment analysis method based on topic influence

Abstract

The invention discloses a microblog topic emotion analysis method based on topic influence. The method comprises the following steps: collecting personal information data and microblog text data of amicroblog user; Preprocessing the data; Using an LDA model to carry out theme clustering; Selecting microblog contents of the topics for emotion polarity labeling and performing training to generate an SVM classifier; Using an SVM classifier to obtain the emotional tendency of the unlabeled text; Calculating microblog user influence factor values and similarity between microblogs and topics, calculating emotion values of the whole topics by combining microblog emotion polarities and topic influence factors, and sequencing the topics according to the emotion values, so as to provide reference for public opinion analysis. The public opinion event and hot topic possibly emerging in the microblog text topics can be found in time, the topic public opinion degrees are ranked according to topic influence, the method can be applied to network public opinion supervision and emergent public opinion event monitoring, and the problems that an existing public opinion system is not supervised in time and blindness are solved.

Description

technical field [0001] The invention relates to a microblog topic sentiment analysis method based on topic influence, and belongs to the technical field of big data and text mining. Background technique [0002] In today's information age, people rely more and more on the Internet, and are more and more accustomed to obtaining information, exchanging information and expressing their personal emotions on social networks; social platforms such as Sina Weibo have a huge number of Weibo users and users The huge data generated through the use of Weibo, behind which these data contain huge commercial and social value. Therefore, the research topics related to Weibo are getting more and more attention from us, and the sentiment analysis of Weibo and the deeper public opinion analysis behind it are the hot spots in these topics. [0003] Sentiment analysis, also known as opinion mining, aims to analyze texts with personal subjective emotions or analysis, mine personal opinions or e...

AI Technical Summary

Problems solved by technology

Using sentiment lexicon to analyze text sentiment, applied to word feature level, has high analysis accuracy, but limited by NLP technology and related extraction technology, it is easy to lose important patterns hidden in the data set; using machine learning methods to analyze is mainly through learning A large amount of data is used to train the classification model. This method is more objective and accurate for sentiment analysis, but it is highly dependent on the training corpus and the training period is long.

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