A method for generating a sentiment data graph based on natural language understanding

By combining natural language understanding and multimodal generation technologies with cross-cultural intent field parameters, the problem of semantic fragmentation and misuse of cultural symbols in the generation of emotional graphics in existing technologies has been solved. This has enabled multi-dimensional graphics generation that is semantically accurate, culturally adapted, and visually coherent, and is applicable to different media scenarios.

CN121706786BActive Publication Date: 2026-05-26DALIAN POLYTECHNIC UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
DALIAN POLYTECHNIC UNIVERSITY
Filing Date
2025-11-25
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies cannot deeply understand the hierarchical structure of text semantics, contextual emotions, and cultural differences in the generation of emotional graphics. This results in semantic fragmentation, distorted emotional expression, and misuse of cultural symbols in the generated results, making it difficult to achieve dynamic consistency between semantics and visuals and multicultural consistency.

Method used

By integrating natural language understanding and multimodal generation technologies, this method collects emotional text, basic brand visual elements, and cross-cultural contextual description data to construct cross-cultural intent field parameters, binds them to multi-domain visual semantic tensor streams, and uses generative visual networks to generate emotional graphics. Dynamic control is then applied to achieve semantically accurate, culturally adapted, and visually coherent graphic generation.

Benefits of technology

It achieves high-fidelity conversion of emotional text into multi-dimensional graphic data, possessing semantic consistency and continuity, enhancing the cultural adaptability and aesthetic consistency of the generated results, and enabling adaptive rendering and consistent output in different media scenarios.

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Abstract

This invention discloses a method for generating emotional data graphics based on natural language understanding, comprising the following steps: collecting emotional text data, brand visual basic element data, and cross-cultural context description data, and processing the collected data; performing natural language understanding on the processed emotional text data; constructing a multi-domain visual semantic tensor flow that evolves over time; constructing a cross-cultural intent field and binding the parameters of the cross-cultural intent field to the multi-domain visual semantic tensor flow; generating a preliminary emotional graphic structure sequence through a generative visual network; dynamically adjusting the preliminary emotional graphic structure sequence; and outputting it to different media scenarios to obtain emotional data graphics. This invention integrates natural language understanding and multimodal generation technologies to achieve emotion-semantic driven graphics generation, possessing the advantages of semantic accuracy, cultural adaptation, and visual coherence.
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