alterlab-text-as-data

From the author

Analyzes text as social-science data — topic modeling (BERTopic with embeddings + class-based TF-IDF, LDA/NMF via scikit-learn or gensim), document embeddings (sentence-transformers), dictionary/lexicon methods, and supervised text classification — choosing the method that matches the inferential goal (discovery vs measurement vs prediction) and validating topic reliability rather than trusting one stochastic run. It uses the verified stack (BERTopic, scikit-learn, gensim CoherenceModel, spaCy, sentence-transformers) with pinned patterns. Use when the request mentions topic modeling, text as data, computational text analysis, document embeddings, dictionary/sentiment lexicons, or classifying a corpus. For training or fine-tuning transformer models prefer alterlab-transformers; for humanities close-reading corpora prefer alterlab-digital-humanities. Part of the AlterLab Academic Skills suite.