An executable survey of text classification algorithms

What it does This repository accompanies the survey paper Text Classification Algorithms: A Survey (arXiv:1904.08067) by providing explanatory text and Python snippets that walk through the full document-classification pipeline. It details text cleaning and preprocessing—tokenization, stop-word removal, stemming, lemmatization, and noise removal—then moves to feature extraction via Word2Vec, GloVe, and ELMo embeddings. The project’s topics suggest it also covers classical methods like Naive Bayes, SVM, and Random Forest alongside neural architectures including CNNs, RNNs, and Hierarchical Attention Networks.

The interesting bit Instead of packaging a single unified framework, the repo behaves like an executable literature review: decades of techniques are gathered in one place where each concept is followed by illustrative code. The breadth is the point—you can watch the field evolve from bag-of-words hygiene to contextualized word vectors without changing repositories.

Key highlights

• Tied to a formal survey paper and backed by 1824 stars, indicating it is a well-known academic reference
• Covers preprocessing fundamentals (tokenization, spelling correction, capitalization pitfalls) alongside modern embeddings
• Bundles or references implementations of Word2Vec, GloVe, and ELMo
• Topics span classical ML (Logistic Regression, SVM, k-NN, Random Forest) and deep-learning approaches (CNN, RNN, Deep Belief Networks, HAN)
• Structured as educational material rather than a production API

Caveats

• The repository is largely a collection of tutorial explanations and disjoint code snippets, not a cohesive installable library
• The README notes a segfault in the Word2Vec compute-accuracy utility
• Because the README is truncated in the sources, it is unclear whether every listed algorithm has a complete implementation or only an explanatory description

Verdict Students and researchers who want a curated, broad map of text-classification techniques will find this a useful on-ramp. Practitioners looking for a single maintained framework should keep scrolling.