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Multi-modal social media sentiment analysis method based on feature fusion

A social media and feature fusion technology, applied in the field of emotion classification, can solve problems such as inability to effectively use the relationship and influence of visual information and text information, and achieve accurate classification results

Pending Publication Date: 2022-02-08
丁健宇
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, most of the existing research cannot effectively use the relationship and influence between visual information and text information.

Method used

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  • Multi-modal social media sentiment analysis method based on feature fusion
  • Multi-modal social media sentiment analysis method based on feature fusion
  • Multi-modal social media sentiment analysis method based on feature fusion

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Embodiment Construction

[0053] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0054] as attached figure 1 As shown, the multimodal social media sentiment analysis method based on feature fusion includes the following steps,

[0055] S1: Preprocessing the multi-modal social media graphic data to be analyzed;

[0056] Specifically, S101: Give each group of graphic data three annotation marks, follow the principle of using two or more identical marks, and remove the interference caused by a small number of unrecognizable data items;

[0057] S102: For the same set of graphic data, completely opposite marks are excluded, the data with positive-neutral mark is negative, and the data with positive-neutral is marked as positive.

[0058] Further, S2: Based on the multi-head attention mechanism, ex...

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Abstract

The invention discloses a multi-modal social media sentiment analysis method based on feature fusion, and the method comprises the following steps: S1, preprocessing to-be-analyzed multi-modal social media image-text data; S2, based on a multi-head attention mechanism, extracting text emotion features from the preprocessed multi-modal social media image-text data; S3, extracting image emotion features from the preprocessed multi-modal social media image-text data by using a deep residual network; and S4, performing sentiment classification and correlation analysis of image-text sentiment tendency according to the extracted text sentiment features and image sentiment features. According to the multi-modal social media sentiment analysis method, the sentiment recognition precision can be remarkably improved, and a new thought is provided for multi-modal social media sentiment analysis.

Description

technical field [0001] The invention relates to the technical field of sentiment classification, in particular to a multimodal social media sentiment analysis method based on feature fusion. Background technique [0002] With the development of information technology, social media has gradually become an integral part of people's lives. People also express different emotions in the process of sharing personal opinions, understanding current affairs news, and keeping track of friends' dynamics through social platforms such as Weibo, Zhihu, and Douban. Sentiment analysis is a core task in natural language processing, which aims to identify the emotional polarity of opinions, sentiments, and evaluations. Sentiment analysis of social media data will not only help scholars to accurately understand people's attitudes and habits in the real world, but also grasp people's choices in fields such as healthcare, political topics, TV and movies, and online shopping. . [0003] Accord...

Claims

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Application Information

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IPC IPC(8): G06F16/33G06F16/35G06F16/55G06F16/583G06N3/04G06N3/08
CPCG06F16/3344G06F16/35G06F16/55G06F16/5846G06N3/08G06N3/045
Inventor 丁健宇祁云嵩马崟桓赵呈祥
Owner 丁健宇
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