Sentiment Classification and Topic Clustering for NetEase Cloud Music Comments
To boost NetEase Cloud Music’s comment handling, the authors combine active‑learning‑driven relabeling, domain‑specific MLM pretraining, contrastive‑learning‑based sample expansion, and multi‑task BERT sharing to raise sentiment‑classification precision and recall above 90 % and double moderation clean‑rate, while employing prompt‑generated story themes, IP‑focused classifiers, and hot‑word aggregation for effective short‑text topic clustering and scalable, theme‑aware distribution.