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textnets review

Free#37 of 37 in Text Analytics Software

A focused open-source choice for Python users studying document–term networks.

5.7/10Editor score
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

For researchers and developers working with document collections, textnets provides a Python package that represents texts as networks linking documents and terms. It is free GPLv3 open-source software designed for local, self-hosted use, with workflows suited to Jupyter notebooks and the Python scientific stack. Its audience is narrow but clear: people performing computational text analysis who want graph-based views of a corpus rather than a general-purpose text-analytics suite.

Its standout capability is the document–term network model. textnets tokenizes text, removes stop words, stems terms, filters punctuation, extracts n-grams and noun phrases, and applies TF-IDF weighting. Users can project bipartite graphs into document-only or term-only networks, then apply Leiden community detection, clustering, centrality measures such as degree, PageRank and BiRank, and network visualization. Graphs and textnets objects can also be saved and loaded. Integrations with spaCy, igraph and Jupyter support a Python-centered research workflow.

Choose textnets when network structure, term relationships and community patterns are central to the analysis. Installation is local through pip or Nix, and self-hosted deployment gives the user responsibility for the runtime environment. The trade-off is breadth: the package does not target sentiment analysis, entity extraction or broader text-analytics workflows. Teams seeking those capabilities, or support beyond documentation, should consider a more general platform. For solo researchers and small or mid-market technical teams comfortable with Python, textnets offers a focused toolkit; organizations wanting managed services or wider language-analysis coverage should look elsewhere.

textnets pros and cons

  • Where it wins
    • Builds document–term and projected networks with centrality metrics.
    • Preprocesses text with tokenization, stemming, n-grams and noun phrases.
    • Works with Python, spaCy, igraph and Jupyter for notebook analysis.
  • Where it doesn't
    • Focuses on network analysis rather than sentiment or entity extraction.
    • Self-hosted deployment puts installation and environment management on the user.
    • Support centers on documentation rather than dedicated assistance.

textnets fact sheet, pricing and score →

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