Neural Network Style Analysis Model for SNS Text
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Solution Overview
Problem
Existing interior services face challenges in efficiently recommending interior products that match a user's preferred style, given the vast amount of information available, which hinders user convenience.
Innovation Solution
A neural network model is developed to analyze user style based on text data from social network services by processing and filtering text data, generating feature vectors, and training a machine learning model to correlate user content with preferred interior styles, enabling personalized product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If interior services provide vast amounts of product information, then product variety increases, but user convenience deteriorates due to difficulty in finding matching products
Solution Approach 1:
The patent replaces manual style assessment methods with an automated neural network-based text analysis system. The system automatically extracts style information from SNS text data, converts it to feature vectors, and generates style scores, eliminating the need for manual user input while providing personalized product recommendations from the vast product information database.
2Ease of manufacture
If manual style assessment methods are used, then implementation simplicity is maintained, but accuracy and personalization capability deteriorate
Solution Approach 1:
The patent introduces an intermediary text analysis system that processes SNS text data through multiple stages: text preprocessing, feature extraction, vector generation, and style scoring. This intermediary layer between raw text data and style assessment enables accurate and personalized style analysis while maintaining automated operation.
3Measurement precision
If automated text analysis is implemented, then style assessment accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the text analysis system into distinct functional modules: text preprocessing module, feature extraction module, vector generation module, and style scoring module. Each module performs a specific function, making the complex automated analysis process more manageable and maintainable while achieving high accuracy in style assessment.
Data Source
AI summary
A method for providing a style analysis model according to one embodiment of the present invention may comprise: obtaining a training document including text data written by a user and determining a first text included in the training document; determining a predetermined number of second texts from among the first text of the training document; generating a first feature vector configured on the basis of the number of times each second text is included in training documents written by each user; generating, for each class, a second feature vector on the basis of the number of times each style-specific text is included in all the obtained training documents; labeling the first feature vector with a class of a second feature vector that is most similar to the first feature vector; and generating and training a machine learning-based neural network model that derives a correlation between the first feature vector and the class labeled in the first feature vector.