stage1-clustering

Automating customer support chat clustering and tagging requires a Python pipeline that processes input Excel files containing user chats, applies language auto-detection, and outputs structured clusters with generated tags and a Stage 2-ready analysis report.

Yes, the clustering pipeline includes automatic language detection that identifies Korean or Japanese from the first user message, ensuring language-aware tagging of customer support chat data throughout the clustering workflow.

Preparing unstructured chat data for analysis involves running an automated Python clustering pipeline that transforms user conversations into structured insights, generating per-cluster tags and validated outputs ready for Stage 2 downstream processing.

Clustering customer support conversations from Excel uses a Python pipeline with pandas, numpy, and scikit-learn to process input files, auto-detect language from the first message, and produce clustered data with tags and an analysis report.

Scikit-learn is used within this clustering pipeline alongside pandas and numpy to automate chat clustering and tagging, while language detection is handled automatically from the first user message in each conversation.

Automated chat clustering currently supports Korean and Japanese language detection from the first user message only, processing Excel file inputs and generating Stage 2-ready reports with per-cluster tags and validated outputs.