| name | recommend-tools |
| description | Recommend well-maintained external libraries and tools for text corpus analysis beyond what this plugin ships — classical NLP, topic modeling, corpus indexing, aspect-based sentiment, multilingual analysis. Use when a task calls for something this plugin doesn't do natively, or when the user wants a survey of what's out there. |
Recommend Tools
A curated catalog. Not every corpus problem should be solved inside Claude Code — sometimes the right move is to point at an established library and set the user up to use it.
Classical NLP toolkits
- spaCy — production-grade NLP: tokenization, POS, NER, dependency parsing, lemmatization. Use as the default for anything
ner-extraction, parametric-analysis, or tokenization-heavy.
- TextBlob — friendly wrapper over NLTK/pattern. Good for quick sentiment, noun-phrase extraction, translation, simple classification on small corpora. Lower quality than spaCy/transformers but very approachable.
- NLTK — classic toolkit, strong for linguistic/academic work. Slower than spaCy for production.
- TextAnalysis.jl — the main NLP library for the Julia ecosystem. Reach for this when the user is already in Julia (scientific computing, notebooks) — covers document representations, TF-IDF, LDA, summarization, sentiment.
Topic modeling & clustering
- BERTopic — default recommendation for
topic-analysis. Embeddings + UMAP + HDBSCAN + c-TF-IDF. Dynamic topics over time built-in.
- gensim — LDA, LSI, word2vec. Use when BERTopic is overkill or when LDA is explicitly wanted.
- Top2Vec — similar space to BERTopic.
Aspect-based sentiment / fine-grained opinion
- PyABSA — aspect-based sentiment analysis framework. Use when the user wants per-aspect sentiment ("the battery is good, but the screen is bad" → {battery: +, screen: -}), not just document-level polarity. Strong for review corpora.
Corpus indexing & search
- BlackLab — Lucene-backed corpus retrieval engine for linguistically annotated corpora. Use when the user has a large annotated corpus and wants structured queries ("find every sentence where a PERSON is the subject of a verb of speech") rather than plain text search. Heavier setup; justifies itself on research-grade corpora.
- Whoosh / Tantivy — lightweight full-text indexes for simpler cases.
Entity extraction (beyond spaCy)
- GLiNER — zero-shot NER with user-specified entity types. Excellent when your entity types are domain-specific and don't match spaCy's defaults.
- flair — strong NER/POS taggers, multilingual.
Embeddings / similarity
Keyword / keyphrase extraction
- KeyBERT — BERT-based keyphrase extraction. Good for tag derivation.
- YAKE — unsupervised, language-agnostic, statistical.
Readability & text stats
- textstat — Flesch, Flesch-Kincaid, Gunning Fog, etc. Used by
parametric-analysis.
When to pick what
- "I want to explore — quick and friendly" → TextBlob or spaCy.
- "I need topic clusters and evolution over time" → BERTopic.
- "I have reviews and want per-feature sentiment" → PyABSA.
- "I'm doing corpus linguistics research" → BlackLab (+ spaCy for annotation).
- "I'm in Julia already" → TextAnalysis.jl.
- "I need zero-shot domain-specific entities" → GLiNER.
Integration
When recommending a tool, show the user a minimal install + a 10-line example that works on their corpus. Don't just drop a link.
