classify-text and 20-newsgroups_text-classification
These are **competitors** — both implement text classification on the identical "20 Newsgroups" dataset using similar classical ML approaches (Naive Bayes), so users would choose one based on code quality, documentation, or implementation details rather than using them together.
About classify-text
yassersouri/classify-text
"20 Newsgroups" text classification with python
Implements comparative experiments across multiple feature representations (Bag of Words, TF, TF-IDF) and classifiers (Naive Bayes, SVM, k-NN) using scikit-learn, with evaluation via train-test splits and stratified k-fold cross-validation. Handles preprocessing quirks like UTF-8 incompatibility in source documents and supports both binary classification (likes vs. dislikes) and full 20-class multiclass scenarios. Results demonstrate TF-IDF with linear SVM achieving ~97% accuracy on binary tasks and ~89% on full 20-class classification.
About 20-newsgroups_text-classification
gokriznastic/20-newsgroups_text-classification
"20 newsgroups" dataset - Text Classification using Multinomial Naive Bayes in Python.
This project helps classify text documents into predefined categories, much like sorting incoming emails or articles into relevant folders. You feed it a collection of text documents, and it outputs which category each document belongs to. This is useful for anyone who needs to automatically organize or understand large volumes of text, such as researchers analyzing public discussions or content managers categorizing news articles.
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