Build text features with NLP
Extract features from text data
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Write Python code to engineer features from a text column [text_column] in DataFrame df. Include: TF-IDF vectors (top 500 features), sentence embeddings using sentence-transformers, readability scores (Flesch-Kincaid), sentiment scores (VADER), entity counts, and text length features. Combine with numerical features for downstream modeling.
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Pour éviter la colinéarité et réduire la dimension, appliquez une sélection de caractéristiques (ANOVA ou importance random forest) après concaténation, surtout si vous combinez TF‑IDF (500 dims) et embeddings (384+ dims).
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