Microblog multimoding sentiment analysis method based on microblog environment

Abstract

The invention discloses a microblog multimoding sentiment analysis method based on a microblog environment. The method is characterized by comprising the steps of obtaining microblog data, obtaining microblog text content and a first modeling text characteristic of commenting content, extracting a second modeling characteristic of the commenting content, combining the first modeling text characteristic and the second modeling text characteristic so as to obtain the final characteristic vector, conducting emotion mark treatment on microblog data so as to obtain a multimoding sentiment training model, and pre-testing the emotion state of the microblog text content to be tested. The method can be used for being further combined with the microblog commenting content for characteristic extraction of the microblog text content so as to improve the judging accuracy of microblog emotion states.

Description

technical field [0001] The invention belongs to the field of natural language processing technology and emotion computing, in particular, it is a microblog multi-modal emotion analysis method based on a microblog group environment. Background technique [0002] With the popularity of information exchange and transaction platforms such as e-commerce, microblog, and online news media, related comments and microblog data have shown a blowout growth. Faced with such a massive amount of data, how to extract useful information that is beneficial to all parties has become a research hotspot in various fields today. As one of the research directions, text orientation judgment is also regarded as one of the important basis for data value analysis. [0003] Microblog, short for microblog, with the rise of social networks, microblog is no longer just a way for people to express their emotions in work and life, but is more used as a way of communication and communication between people...

AI Technical Summary

Problems solved by technology

Among them, most text feature extraction only considers the surface meaning of words, and does not further analyze the text at the semantic level, which leads to inaccurate sentiment analysis

Since microblogs are sentence-level texts, the selection of features in sentiment analysis is also limited by the space. The existing microblog sentiment analysis methods only extract the features of microblog text content, while ignoring the location of microblog text. The emotional environment of Weibo, that is, does not consider the emotional expression of Weibo text content from the perspective of Weibo comments, and the interaction between Weibo users and Weibo friends

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