Real-Time Screen Sports Satisfaction Prediction From Chat Features
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Solution Overview
Problem
Existing methods fail to accurately analyze user satisfaction in real-time for screen sports content experiences involving multiple users, as they do not effectively utilize real-time interaction data such as chat data to comprehensively understand user emotions.
Innovation Solution
A method and device that utilize a text feature recognition model and a language feature recognition model to generate satisfaction detection information from real-time chat data, followed by a satisfaction prediction model to predict user satisfaction levels based on these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple learning models are used to analyze text and language features, then user satisfaction prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The satisfaction analysis system is divided into multiple specialized learning models: a text feature recognition model for extracting textual characteristics and a language feature recognition model for analyzing linguistic patterns. Each model focuses on specific aspects of chat data, allowing for more precise satisfaction prediction while maintaining modular architecture that manages complexity through functional division.
2Loss of time
If real-time chat data is analyzed using multiple models, then user satisfaction can be predicted in real-time, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of chat data by extracting text features and language features separately through dedicated recognition models before final satisfaction prediction. This pre-processing approach prepares data in advance in a structured format, enabling faster real-time analysis when chat messages are received, as the feature extraction pipelines are already optimized and ready to process incoming data streams.
Data Source
AI summary
The present disclosure provides a method and device for analyzing user satisfaction of screen sports contents. According to one embodiment, the present disclosure provides a method of analyzing user satisfaction of a screen sports content, including generating first satisfaction detection information based on real-time chat data of a user using a text feature recognition model, generating second satisfaction detection information based on real-time chat data of the user using a language feature recognition model, and predicting real-time satisfaction of the user based on the first satisfaction detection information and the second satisfaction detection information using a satisfaction prediction model.