Comment Opinion Classification via Automatic Training Data Generation
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Solution Overview
Problem
The existing methods for comment information processing in e-commerce platforms require significant manual labor for data labeling, leading to high costs and inefficiencies, especially in handling diverse comment expressions and latent opinions, which hampers user experience and data sharing across different application scenarios.
Innovation Solution
A method that determines opinion categories for each opinion phrase in a comment opinion dictionary, generates training samples by matching these categories with target corpora, and trains a classification model to identify opinion categories, thereby reducing the need for extensive manual labeling and labor costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised sequence labeling is used for opinion extraction, then opinion extraction accuracy is improved, but manual labeling workload increases significantly
Solution Approach 1:
The system performs preliminary action by automatically generating opinion phrases and their corresponding categories before the actual opinion extraction task. The opinion phrase generation module creates a dictionary of opinion phrases with categories in advance, which serves as training data for the sequence labeling model, eliminating the need for manual labeling in each application scenario.
Solution Approach 2:
The system implements self-service by enabling the model to automatically generate opinion phrases and categories without human intervention. The opinion phrase generation module uses the trained classification model to autonomously create training data, and the system continuously improves itself through automatic learning from new comment data.
2Measurement precision
If customized opinion classification systems are created for different scenarios, then classification accuracy is improved, but data sharing capability deteriorates
Solution Approach 1:
The system achieves universality by creating a unified opinion phrase generation module that serves multiple application scenarios. The classification model trained on general comment data can be applied across different e-commerce domains (electronics, clothing, food, etc.), allowing one model to perform multiple classification tasks without scenario-specific customization.
Solution Approach 2:
The system performs preliminary action by pre-training the classification model on diverse comment corpora from multiple domains before deployment. This preliminary training enables the model to learn universal opinion patterns that can be applied across different scenarios, eliminating the need for scenario-specific customization while maintaining high classification accuracy.
3Adaptability or versatility
If extensive manual labeling is performed for different application scenarios, then model adaptability is improved, but labor costs increase significantly
Solution Approach 1:
The system implements self-service by automatically generating training data through the opinion phrase generation module. Instead of requiring manual labeling for each application scenario, the system autonomously creates opinion phrases with categories using the trained classification model, thereby adapting to new scenarios without human labor while maintaining model adaptability.
4Measurement precision
If supervised sequence labeling is used for opinion extraction, then extraction precision is improved, but processing efficiency deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training the sequence labeling model with automatically generated opinion phrases and categories. This preliminary training phase creates a robust model that can perform accurate opinion extraction without requiring manual labeling during actual processing, thereby maintaining high extraction precision while improving processing efficiency through automation.
Data Source
AI summary
The present disclosure discloses a comment information processing method and apparatus, and a medium. The specific implementation solution is: in response to a user operation, determining an opinion category corresponding to each opinion phrase in a comment opinion dictionary; obtaining a target corpus matching each opinion phrase from a plurality of comment corpora; for each opinion phrase, using a corresponding opinion category to label the target corpus matching each opinion phrase to obtain a first training sample; and training a classification model with the first training sample to identify the opinion category of a comment by using a trained classification model.