Analyze issue and set up todo list
Read CLAUDE.md for repo guidelines (no CLAUDE.md found — used repo conventions from existing README)
Explore repository structure
Read ml_sentiment.py thoroughly (9 methods, 917 lines)
Read related files for context (ml_cvbow.py, ml_yf_nlp_orchestrator.py, diagrams/ml_sentiment_flow.mermaid, docs/NLP_sentiment_final_results.json)
Create comprehensive README for ml_sentiment.py → README_ml_sentiment.md
Commit the new README
Push to branch

README_ml_sentiment.md — 689 lines of complete documentation covering:

Overview and position in the Bespin pipeline with ASCII architecture diagram
All dependencies (HuggingFace, NLTK, sklearn, ZSTD, pandas, etc.) with the exact model name (mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis)
All 9 methods with full parameter tables, return value descriptions, and behavioral notes:
- __init__ — initialization and all instance attributes
- preload_classifier / _bg_load_worker / _get_classifier — thread-safe singleton lifecycle
- compute_sentiment — main entry point, C4 vs BS4 dispatch paths
- unified_chunker — word-boundary-aware text chunking algorithm
- dict_processor — HuggingFace inference loop and JSON package assembly
- nlp_sent_engine — stopword removal, high-frequency word extraction, DF update
- save_sentiment_df — DataFrame schema and append logic
- sentiment_metrics — 2-vector quant-standard aggregation model
- sentiment_direction — sentiment band system with 9 bands and "Approaching" logic
- sentiment_vector_model — conviction score computation
- zstd_text_compressor — ZSTD compression with v1/v2 architecture notes
Output format (cr_package) with real example data from docs/NLP_sentiment_final_results.json
Thread safety explanation with annotated flow
Usage example with code

New readme for ml_sentiment #10

Description