Deep learning-based picture-text fusion microblog emotion analysis method

A sentiment analysis and deep learning technology, applied in the field of sentiment analysis, can solve the problems of limited coverage of sentiment dictionaries, poor performance, and difficulty in coping, and achieve the effects of representativeness, improved accuracy, and fast convergence.

Inactive Publication Date: 2018-08-10
GUILIN UNIV OF ELECTRONIC TECH
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AI Technical Summary

Problems solved by technology

The method based on the emotional dictionary first constructs the emotional dictionary, and then calculates the emotional polarity of the new word according to the similarity between the new word and the words in the emotional dictionary. This method is limited by the coverage of the emotional dictionary, especially for Weibo, where new words appear frequently Social media is even more difficult to deal with; machine learning-based methods mainly use machine learning models such as SVM, K-means, and NB to extract Weibo emotional features. However, due to the randomness and brevity of Weibo, these methods are often ineffective

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  • Deep learning-based picture-text fusion microblog emotion analysis method
  • Deep learning-based picture-text fusion microblog emotion analysis method
  • Deep learning-based picture-text fusion microblog emotion analysis method

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Embodiment

[0048] refer to figure 1 , figure 2 , a deep learning-based image-text fusion microblog sentiment analysis method, including the following steps:

[0049] S1 collects graphic and text microblog data and performs preprocessing: collects graphic and text microblog data from microblog and performs preprocessing;

[0050] S2 Extracting the emotional features of graphic microblog text: using a two-way long-short-term memory neural network to extract the emotional features of graphic microblog text;

[0051] S3 extracts the emotional features of graphic and text microblog pictures: using convolutional neural network to extract the emotional features of graphic and text microblog pictures;

[0052] S4 conducts emotional analysis of micro-blog with graphic-text fusion: integrates the text emotional features obtained in step S2 and the image emotional features obtained in step S3 to construct a graphic-text micro-blog sentiment classification model, and performs graphic-text fusion ...

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Abstract

The invention discloses a deep learning-based picture-text fusion microblog emotion analysis method. The method is characterized by comprising the following steps of S1, collecting picture-text microblog data and performing preprocessing; S2, extracting text emotion features of a picture-text microblog; S3, extracting picture emotion features of the picture-text microblog; and S4, performing picture-text fusion microblog emotion analysis. According to the method, in combination with pictures and texts in the microblog, an emotional tendency of a user can be judged more accurately, and the accuracy of emotion analysis can be improved.

Description

technical field [0001] The invention belongs to the field of sentiment analysis, and in particular relates to a microblog sentiment analysis method based on deep learning of graphic and text fusion. Background technique [0002] With the development of network technology and the popularization of mobile devices, people interact more frequently with information on the Internet, and the number of users of major social networking platforms is also increasing. Social networking platforms such as Sina Weibo, Twitter and Facebook have become An essential tool for people to voice their opinions and document their lives. Mining the emotions contained in the huge amount of information published by users on social platforms will help public opinion analysis, personalized recommendation and personalized search. Therefore, microblog sentiment analysis research has great scientific research value and application value for academia and industry. . [0003] Most of the existing sentiment...

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Application Information

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IPC IPC(8): G06F17/21G06F17/27G06F17/30G06K9/62G06N3/04G06N3/08
CPCG06F16/9535G06N3/08G06F40/117G06F40/289G06N3/045G06F18/25
Inventor 缪裕青汪俊宏刘同来蔡国永文益民缪永进邹魏
Owner GUILIN UNIV OF ELECTRONIC TECH
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