Sentiment Analysis Model Training with Automated Tag Generation

The patent applies preliminary action by pre-establishing a tag generation model before training the sentiment analysis model. This tag generation model automatically generates tag information for untagged sample data, preparing the data in advance without requiring manual annotation during the main training process. This resolves the contradiction by performing the labor-intensive tagging work automatically before the actual model training, reducing resource consumption while maintaining model accuracy.

The patent implements self-service through the tag generation model that automatically generates tag information for untagged sample data using pre-trained word vectors and classification algorithms. The system serves itself by automatically creating training data without external manual intervention, thereby reducing manpower resources while ensuring sufficient training data for accurate model performance.

The patent performs preliminary action by pre-training word vectors and establishing the tag generation model before the sentiment analysis model training. This preparation phase automatically creates a comprehensive distribution of sentiment words and sentence patterns through algorithmic processing, eliminating the need for time-consuming manual annotation while ensuring wide coverage for effective training.

The patent replaces the mechanical manual annotation system with an automated computational system. The tag generation model uses pre-trained word vectors, text segmentation, and classification algorithms to automatically generate tag information, substituting human manual work with automated mechanical processes that are faster and more scalable, thereby reducing time loss while maintaining training effectiveness.

The patent applies merging by combining multiple tag generation models (first, second, and third tag generation models) into a unified sentiment analysis training system. Each model focuses on different aspects (all words, sentiment words, non-sentiment words), and their outputs are integrated to generate comprehensive tag information. This resolves the contradiction by merging multiple specialized models into a coordinated system that improves accuracy while managing complexity through functional specialization.

The patent implements segmentation by dividing the tag generation task into three separate models, each handling different aspects: the first model processes all words, the second focuses on sentiment words, and the third handles non-sentiment words. This segmentation allows each model to specialize and achieve higher accuracy in its specific domain while the overall system manages complexity through modular architecture.