Multi-modal session data analysis method and system based on time axis fusion analysis

By using a time-axis fusion analysis method, the time axis of multimodal conversation data is calibrated, key information nodes are identified, and sentiment maps are generated. This solves the efficiency and consistency problems in conversation data analysis, realizes the structuring of conversation content and the visualization of sentiment changes, and improves the accuracy and traceability of the analysis.

CN121979977APending Publication Date: 2026-05-05GUANGZHOU YUESHU INFORMATION TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202512015584.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-29
Publication Date
2026-05-05

AI Technical Summary

Technical Problem

Existing technologies for conversation data analysis suffer from problems such as low efficiency of manual annotation, high subjectivity, poor consistency of analysis results, and lack of quantitative expression in sentiment analysis, making it difficult to track the trajectory of sentiment changes and the dynamic correlation of key events.

Method used

By using a time-axis-based fusion analysis method, multimodal conversation data is acquired, the modal conversation time axis is calibrated, a unified wall clock time axis is generated, key information nodes are identified, and the emotional intensity curve is calculated to generate an interactive emotional map.

Benefits of technology

It improves the accuracy and objectivity of multimodal conversation data analysis, enhances the traceability of analysis results, and enables accurate temporal modeling of the conversation process and visualization of emotional changes.

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Abstract

The invention relates to the technical field of session data analysis, and discloses a time axis fusion analysis-based multi-modal session data analysis method and system, and the method comprises the steps: obtaining multi-modal session data, and enabling each modal session data in the multi-modal session data to have a corresponding modal session time axis; according to a preset anti-interference fitting algorithm, all modal session time axes are calibrated, and a unified wall clock time axis of the multi-modal session data is generated; identifying key information nodes in the multi-modal session data based on a unified wall clock time axis, and calculating an emotion vector corresponding to each moment in the multi-modal session data to obtain an emotion intensity curve of the multi-modal session data; and according to all the key information nodes and the emotional intensity curve, generating an interactive emotional map of the multi-modal session data. Therefore, by implementing the multi-modal session data analysis method, the traceability of the analysis result can be improved while the analysis accuracy and objectivity of the multi-modal session data are improved.
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