Multi-Label Text Classification

Multi-label text classification is a type of classification problem in machine learning that allows each instance to be assigned multiple labels, rather than a single category. The goal of this task is to predict all relevant categories to which the given text data may belong. Unlike multi-class classification, multi-label classification does not limit the number of labels that can be assigned to an instance, making it more flexible and practical for handling complex, multi-dimensional data. Multi-label text classification is widely used in sentiment analysis, news categorization, medical diagnosis, and other fields, enabling a more accurate capture and expression of the multiple attributes and meanings of text.