Semi Supervised Text Classification 1

Semi-supervised text classification is a technique in natural language processing aimed at improving the accuracy and efficiency of text classification by utilizing a small amount of labeled data and a large amount of unlabeled data. This method combines the advantages of supervised and unsupervised learning, enabling it to enhance the model's generalization ability while reducing the cost of manual annotation. Semi-supervised text classification has significant application value in scenarios such as sentiment analysis, topic classification, and spam email filtering, effectively addressing the issue of insufficient labeling in large-scale datasets.