Our text classification datasets rely on expert manual annotation, covering emotion tendency, topic classification, industry attribution, intention labeling and multi-dimensional classification tasks. Massive samples include short comments, long articles, consultation dialogues, public opinion content and industry documents, with unified classification rules and standardized label systems.

We cover multilingual resources, balance sample quantity of each category, effectively reduce training data bias, and include edge and ambiguous samples to enhance model classification ability.

All labeled data pass multi-round cross-review to ensure classification accuracy and label consistency.

We support custom classification dimensions, exclusive industry corpus labeling and multi-label composite classification customization. Widely applied to content screening, public opinion analysis, user intention classification, industry text sorting and content tagging for large models, our datasets help enterprises quickly build high-precision text classification models, realize automatic content management and intelligent information classification.