Method for analyzing emotional polarity of heterogeneous migration images based on multimodal depth potential correlation

Abstract

The present invention provides a method for analyzing emotional polarity of heterogeneous migration images based on multimodal depth potential correlation. The method comprises the following steps: 1)constructing an initial emotional image dataset, and taking the emotional polarity corresponding to the emotional words as the emotional polarity tag; 2) removing the noise data in the initial emotion image data set, and removing the noise by using the method of emotional consistency and the probabilistic sampling model based on the multimodal deep convolution neural network; 3) constructing theheterogeneous migration model based on the multimodal depth potential correlation, and then training the source domain text and the target domain image; 4) constructing the multimodal embedded space,embedding semantic information of the source domain text into the target domain image; and 5) training the image emotional polarity classifier for the image emotional polarity analysis. According to the method provided by the present invention, the obtained data is large in scale, the labor cost is low, the data noise is small, the prediction accuracy is high, the model is strong in interpretability and has strong classification capability, and a better image emotional polarity analysis effect can be reached.

Description

technical field [0001] The invention relates to the technical field of image content understanding and data analysis, in particular to a method for analyzing the emotional polarity of heterogeneous migration images based on multimodal deep latent correlation. Background technique [0002] With the popularity of social media, social networks have an irreplaceable position in people's daily life. More and more social media users are more inclined to use visual content to express their opinions and share their experiences, and a large number of user-generated images are also generated. Facing the huge number of user-generated images, how to mine user-generated image data? The academic and commercial value of the image has become an urgent problem to be solved in academia and industry, especially mining and analyzing user opinions and emotions in the data. Therefore, using user-generated image data for opinion mining and sentiment analysis has become a research hotspot. [0003...

AI Technical Summary

Problems solved by technology

[0005] Recently, some researchers have begun to study the problem of multimodal social media sentiment analysis by combining visual content and text information. Although these methods achieve better results than those that only consider visual features, they ignore the visual and textual modalities. Structural correlation between modalities, exploiting the correlation between visual and textual features associated with images has improved the performance of some cross-modal retrieval and image annotation tasks, but the effectiveness in image sentiment analysis has not been fully investigated. prove

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