KH Coder vs spaCy

KH Coder is software for quantitative content analysis, text mining, and computational linguistics. It can be used with free-response survey answers, interview records, newspaper articles, and other text. The analysis options include word-frequency lists, keyword searches, KWIC, cluster analysis, multidimensional scaling, co-occurrence networks, correspondence analysis, self-organizing maps, and coding cross-tabulations. Visualizations include network views with betweenness centrality and community detection, correspondence-analysis plots, heatmaps, line charts, and bubble plots. Statistical analyses can be viewed with the source-text context, and analysis can run without an internet connection. The English site lists text support for Catalan, Simplified Chinese, Dutch, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, Slovenian, and Spanish. KH Coder runs on Linux, macOS, and Windows, with on-premise deployment. Users can customize it by editing R and MySQL code. The free Windows Starting Edition limits analyzable data and some features and is an older beta; the paid Base Edition is presented as the full version. The Mac Edition costs $99.00 USD once and requires Apple silicon, macOS 11 or later, and 5 GB of available disk space.

spaCy is a free, open-source Python library for building natural language processing applications, with a focus on production use and large volumes of text. Its components handle named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphology, and entity linking. The project lists support for 75+ languages and 84 trained pipelines for 25 languages, though trained pipelines are not available for every language. Developers can configure and train custom pipelines, describe training runs with configuration files, and package pipelines for installation as Python packages. spaCy can use pretrained transformers through spacy-transformers, and supports custom models in PyTorch, TensorFlow, and other frameworks. spaCy Projects supports workflows for downloads, commands, checksum verification, and caching, along with integrations for tracking, demos, model serving, distributed training, and Hugging Face Hub. Installation is available through pip, conda, or source on Linux, macOS, and Windows. The library can run on a user's servers without data leaving them. It is not a hosted software-as-a-service product or web application. The library is free under AGPL or a commercial license; transformer models require a GPU to run effectively.