Multi-modal sentiment analysis method based on multi-dimensional low-rank decomposition
A sentiment analysis, low-rank decomposition technology, applied in the field of computer vision, to achieve good application value, improve performance, and improve the effect of accuracy
Active Publication Date: 2021-02-05
ZHEJIANG UNIV
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This method makes full use of various modalities in video data, and overcomes the shortcomings of existing tensor methods that ignore timing information. In addition, this method has relatively good scalability
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[0033] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0034] On the contrary, the invention covers any alternatives, modifications, equivalent methods and schemes within the spirit and scope of the invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the detailed description of the present invention below. The present invention can be fully understood by those skilled in the art without the description of these detailed parts.
[0035] refer to figure 1 , in a preferred embodiment of the present invention, the multi-modal sentim...
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The invention discloses a multi-modal sentiment analysis method based on multi-dimensional low-rank decomposition, which is used for fusing high-dimensional multi-modal features into a low-dimensionalvector and then applying the low-dimensional vector to video sentiment analysis. The method specifically comprises the following steps: obtaining a video data set for training a multi-modal emotion analysis model, the video data set comprising a plurality of sample videos, and defining an algorithm target; extracting image features, audio features and text features in the video data set to obtainimage features, audio features and text features of the video data; establishing a multi-modal sentiment analysis model based on a multi-dimensional low-rank decomposition mechanism based on the extracted image features, audio features and text features; and performing sentiment analysis on an input video by using the multi-modal sentiment analysis model. The invention is suitable for multi-modalemotion analysis of a real video scene, and has good effect and robustness for various complex conditions.
Description
technical field [0001] The invention belongs to the field of computer vision, in particular to a multi-modal sentiment analysis method based on multi-dimensional low-rank decomposition. Background technique [0002] In today's society, video has become an indispensable part of human society, it can be said that it is everywhere. Such an environment has made people's research on the semantic content of video has also been greatly developed. Multimodal sentiment analysis is a relatively important branch of video analysis. In the era of rapid development of short video live broadcast in today's society, multimodal sentiment analysis is particularly important. It can judge the emotional changes of the video speaker in real time based on the expression, language, and voice of the video speaker, and serve subsequent applications. [0003] Most of the existing tensor-based multimodal sentiment analysis methods use the mean pooling of features of different modalities, and then map...
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Login to View More IPC IPC(8): G06K9/00G06K9/62G06F40/284G10L25/03G10L25/63
CPCG06F40/284G10L25/63G10L25/03G06V20/41G06V20/46G06F18/214G06F18/253
Inventor 金涛李英明张仲非
Owner ZHEJIANG UNIV