Multimedia Comment Emoticon Retrieval via Deep Learning
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Solution Overview
Problem
Existing methods for commenting on multimedia resources using text or simple emojis result in monotonous comments and low user interaction, as they do not effectively leverage the content of the multimedia resource.
Innovation Solution
A method that involves receiving a comment trigger operation on a multimedia resource, obtaining emoticons corresponding to the resource, and displaying them to enable users to comment using relevant and diverse emoticons, which are pre-stored or retrieved from a service server based on the resource's category determined through deep learning models analyzing the content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text or simple emojis are used for commenting on multimedia resources, then the commenting process is simple and easy to operate, but the comments become monotonous and user interaction is low
Solution Approach 1:
The system performs preliminary action by automatically analyzing the multimedia resource content through deep learning models to determine resource categories and retrieve relevant emoticons before the user needs to comment. This pre-processing eliminates the need for users to search or type trivially, maintaining ease of operation while significantly improving comment diversity and relevance.
2Adaptability or versatility
If users manually search for or type comments, then they can express any idea, but the process becomes time-consuming and trivial operations increase
Solution Approach 1:
The system provides self-service by automatically analyzing the multimedia resource, determining its category, and retrieving appropriate emoticons without requiring user input for search queries or selections. Users simply trigger the commenting function and receive pre-matched emoticons, eliminating time-consuming manual search operations while maintaining expressive freedom through the automatically selected relevant emoticons.
3Measurement precision
If emoticons are selected based on resource category analysis, then comment relevance is improved, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary layer of deep learning models that automatically analyze multimedia resource content and map it to resource categories, which then serve as intermediaries for selecting appropriate emoticons. This intermediary mechanism handles the complexity of content analysis and category mapping, allowing the system to achieve high comment relevance without exposing users to or requiring manual handling of complex analysis processes.
Data Source
AI summary
The disclosure provides a method for commenting on a multimedia resource, a related electronic device and a storage medium. In response to a comment trigger operation on a multimedia resource, the electronic device obtains an emoticons corresponding to the multimedia resource and displays the emoticons to enable a user account to comment on the multimedia resource.