Multi-modal sentiment analysis method based on DMLANet

A sentiment analysis, multimodal technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve problems such as social media content that is difficult to capture multiple modes, single mode, etc., and achieve multiple improvements. Modal learning, the effect of good sentiment classification results

Pending Publication Date: 2022-03-22
山西三友和智慧信息技术股份有限公司
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Problems solved by technology

[0004] In view of the problems that the above method focuses on a single mode and it is difficult to capture social media content of multiple modes, the present invention provides a method with multi-modal features and can obtain better emotional classification results

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  • Multi-modal sentiment analysis method based on DMLANet

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Embodiment Construction

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0020] A multimodal sentiment analysis method based on DMLANet, such as figure 1 shown, including the following steps:

[0021] S100, data set selection: select two data sets that independently contain 5129 image-text representations represented by an annotator to form a multi-view sentiment analysis data set;

[0022] S200. Network training: input the data set into the deep multi-level attention network DMLANet for training;

[0023] S300. Network verificat...

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Abstract

The invention belongs to the technical field of multi-modal sentiment analysis, and particularly relates to a DMLANet-based multi-modal sentiment analysis method, which comprises the following steps of data set selection, network training and network verification. In the data set selection, two data sets independently comprising 5129 image-texts represented by an annotator are selected to form a multi-view sentiment analysis data set; in the network training, a data set is input into a deep multi-level attention network DMLANet for training; and the network verification verifies the performance of the multi-modal sentiment analysis of the network DMLANet in a deep and multi-level manner.

Description

technical field [0001] The invention belongs to the technical field of multimodal sentiment analysis, and in particular relates to a multimodal sentiment analysis method based on DMLANet. Background technique [0002] Existing methods for multimodal sentiment analysis only focus on a single mode and cannot capture multiple modes of social media content. And in multimodal learning, most works focus on simply combining these two modalities without exploring their complex interrelationships, which leads to multimodal sentiment classification performance not reaching unsatisfactory Effect. [0003] Reasons for problems or defects: At present, multimodal sentiment analysis methods have attracted more and more attention and have broad application prospects, but existing methods focus on a single mode and cannot capture social media content of multiple modes. Contents of the invention [0004] In view of the problems that the above method focuses on a single mode and it is diff...

Claims

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Application Information

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IPC IPC(8): G06V10/80G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06N3/045G06F18/2414G06F18/253
Inventor 潘晓光焦璐璐令狐彬宋晓晨韩丹
Owner 山西三友和智慧信息技术股份有限公司
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