Tibetan language-based multi-modal emotion calculation method and system

Abstract

The embodiment of the invention provides a Tibetan language-based multi-modal emotion calculation method and system, and a server. The method comprises: firstly, obtaining Tibetan language data to beclassified; collecting video signals, voice signals and text information from the Tibetan language data; then, extracting high-level video features, high-level voice features and text features in a classification emotion corpus, respectively extracting the high-level video features, the high-level voice features and the text features, performing learning based on a deep learning model to obtain high-level fusion features, and finally, classifying the high-level fusion features in the classification emotion corpus based on SVM and storing the high-level fusion features in the classification emotion corpus. Therefore, the blank state of the Tibetan language in sentiment analysis can be filled. A basic corpus is provided for Tibetan multi-modal sentiment analysis. The Tibetan language data sentiment recognition method based on the three modes is beneficial to development of Tibetan language multi-mode sentiment analysis, the natural language processing capacity and the intelligent sentiment recognition capacity of the Tibetan language can be promoted, the artificial intelligence information processing capacity of the Tibetan language is improved, and in addition, the sentiment recognition rate of the Tibetan language data can be effectively increased under the condition of mutual fusion of the three modes.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a Tibetan-based multi-modal emotional computing method and system. Background technique [0002] Emotional corpus is the basis of sentiment analysis technology. Currently, there are many single-modal emotional corpora and they are relatively complete. Single-modal corpora are mainly text, voice, and facial expressions, and some corpora involve body gestures, physiological data, etc. However, no matter what kind of unimodal corpus is used for emotion recognition, it has its own limitations, so it is extremely necessary to construct a multimodal emotional corpus. [0003] As an important language, Tibetan is basically blank in sentiment analysis. The construction of multimodal emotional corpus based on Tibetan can provide basic corpus for Tibetan multimodal sentiment analysis, which is beneficial to Tibetan multimodal sentiment analysis. The development of the Tibetan language ...

AI Technical Summary

Problems solved by technology

But no matter what kind of unimodal corpus has its own limitations when performing emotion recognition, it is extremely necessary to build a multimodal emotional corpus

Images