Multi-mode electric power customer emotion analysis method and system based on text and voice

By combining BERT and Transformer models, extracting and fusion of text and speech data features, the accuracy of power customer sentiment analysis in the prior art is solved, and more efficient emotion recognition and personalized services are achieved.

CN119719854APending Publication Date: 2025-03-28STATE GRID JIANGSU ELECTRIC POWER CO LTD MARKETING SERVICE CENT
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Patent Information

Application Number
CN202411802573.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-09
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The prior art is difficult to accurately analyze the emotional state of power customers, especially in the combination of text and voice data, and insufficient timing capability and context information extraction.

Method used

The text emotion recognition model based on BERT and the speech emotion recognition model based on Transformer are used to extract the features of text and speech data through the attention mechanism, and weighted fusion is performed, and finally emotional tendency annotation is performed through the conditional random field CRF layer.

Benefits of technology

It realizes more accurate identification of the emotional status of power customers, improves the accuracy and effect of emotion analysis, and can better meet the personalized service needs of power customers.

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Abstract

The invention discloses a text and voice-based multi-modal electric power customer emotion analysis method and system, and the method comprises the steps: collecting customer service text data and customer service voice data, and carrying out the preprocessing; inputting the preprocessed customer service text data into a BERT-based text emotion recognition model to extract features of a text data modal; inputting the preprocessed customer service voice data into a voice emotion recognition model based on Transform, and extracting features of a voice data modal by using a multi-head self-attention mechanism; based on a cross-modal attention mechanism, performing multi-modal fusion on the features of the text and voice data modals to realize alignment and splicing of the features of the text and voice data modals; and marking the emotion polarity and emotion intensity output by the classification model through a conditional random field (CRF) layer according to the aligned and spliced features of the text and voice data modes. According to the method, the emotion can be effectively labeled, so that the emotion tendency can be more accurately judged.
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