Why evaluation design matters | Stratified folds | Cross-validation loop | Interpret the metrics | From tutorial to research
Package: textclassificationtutorial 0.1.2
textclassificationtutorial: Reproducible Text Classification Workflows
Dependency-light tools and tutorials for teaching reproducible text classification. The package covers HTML text extraction, sentence segmentation, text preprocessing, document-term matrices, TF-IDF, keyword extraction, cosine similarity, stratified cross-validation, classification metrics, and a multinomial Naive Bayes classifier. It modernizes the code accompanying Kobayashi, V. B., Berkers, H. A., Mol, S. T. Kismihok, G., and Den Hartog, D. N. (2017) <doi:10.1177/1094428117719322> The package replaces the original scripts in the paper.
Authors:
textclassificationtutorial_0.1.2.tar.gz
textclassificationtutorial_0.1.2.zip(r-4.7-any)textclassificationtutorial_0.1.2.zip(r-4.6-any)textclassificationtutorial_0.1.2.zip(r-4.5-any)
textclassificationtutorial_0.1.2.tgz(r-4.6-any)textclassificationtutorial_0.1.2.tgz(r-4.5-any)
textclassificationtutorial_0.1.2.tar.gz(r-4.7-any)textclassificationtutorial_0.1.2.tar.gz(r-4.6-any)
textclassificationtutorial_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
textclassificationtutorial/json (API)
| # Install 'textclassificationtutorial' in R: |
| install.packages('textclassificationtutorial', repos = c('https://vkobayashi.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/vkobayashi/textclassificationtutorial/issues
Last updated from:9338ed09b2. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 122 | ||
| source / vignettes | OK | 201 | ||
| linux-release | OK | 119 | ||
| macos-release | OK | 150 | ||
| macos-oldrel | OK | 194 | ||
| windows-devel | OK | 77 | ||
| windows-release | OK | 68 | ||
| windows-oldrel | OK | 66 | ||
| wasm-release | OK | 102 |
Exports:classification_metricscosine_similaritydocument_term_matrixextract_html_dirextract_html_textextract_keywordsf_measurefit_naive_bayespreprocess_textread_label_filesplit_sentencesstratified_foldstf_idf
Dependencies:
Evaluating a Text Classifier
Last update: 2026-08-26
Started: 2026-08-26
Getting Started with Text Classification
Overview | Extract text from HTML | Segment and normalize | Create document features | Explore similarity | Train a classifier | Reproducible research checklist
Last update: 2026-08-26
Started: 2026-08-26
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Calculate binary classification metrics | classification_metrics |
| Compute cosine similarity | cosine_similarity |
| Construct a document-term matrix | document_term_matrix |
| Extract text from a directory of HTML files | extract_html_dir |
| Extract readable text from HTML | extract_html_text |
| Extract top TF-IDF keywords | extract_keywords |
| F-measure | f_measure |
| Fit a multinomial Naive Bayes text classifier | fit_naive_bayes |
| Normalize text for document-term analysis | preprocess_text |
| Read a one-label-per-line file | read_label_file |
| Split text into sentences | split_sentences |
| Create stratified cross-validation folds | stratified_folds |
| Calculate TF-IDF weights | tf_idf |