An Improved Method for Text Classification Using Contrastive Learning

Quality objection texts provided by customers are crucial for steel e-commerce platforms. How to classify these texts and quickly provide corresponding solutions is very important. This article presents an approach based on deep learning for classifying steel quality objection text. With the help of deep learning, the proposed model can automatically extract semantic features from unstructured text without manual intervention. Different from traditional methods, to further improve the model’s performance, this article employs contrastive learning to learn more robust representations. The evaluation of this approach on a large dataset of steel quality objection text demonstrates that it outperforms traditional methods and achieves excellent results. The proposed approach enables the identification of quality issues quickly, which is of utmost importance for the steel industrial practical implications. By reducing the time and resources required to identify quality objections, the proposed approach can help to improve the efficiency of the steel industry.