# kk7nc/Text_Classification

Text Classification Algorithms: A Survey

Repository: https://github.com/kk7nc/Text_Classification
Canonical: https://ross.abutalabs.com/products/kk7nc-text_classification
Language: Python
License: MIT
License Family: permissive
Topics: text-classification, nlp-machine-learning, document-classification, text-processing, dimensionality-reduction, rocchio-algorithm, boosting-algorithms, logistic-regression, naive-bayes-classifier, k-nearest-neighbours, support-vector-machines, decision-trees, random-forest, conditional-random-fields, deep-learning, deep-neural-network, recurrent-neural-networks, convolutional-neural-networks, deep-belief-network, hierarchical-attention-networks
Last push: 2025-04-01T00:35:13+00:00

## Health v2 (maintenance only)
Score: 39/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 14, release rhythm 35, longevity 100
- inputs: {"age_days": 2981, "days_push": 520, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1820, forks 538 (observed 2026-08-28T04:05:40.890213+00:00)

## What it is
A companion repository for the survey paper 'Text Classification Algorithms: A Survey', providing explanations and Python code for text classification techniques. It covers text preprocessing, feature extraction, and classical and deep learning classification algorithms.

## Use cases
- learn text classification algorithms from a survey with code
- understand text preprocessing like tokenization and stop word removal
- compare classical classifiers like naive bayes and SVM for documents
- get started with deep learning models for text classification
- find reference implementations of word embedding and feature extraction
- study RNN and CNN approaches to document classification

## When to choose
- you want a guided, paper-backed introduction to text classification methods
- you need example Python code for NLP preprocessing and classification baselines
- you are surveying classical and deep learning text classifiers before picking one

## When to avoid
- you need a production-ready text classification library or service
- you want state-of-the-art transformer fine-tuning pipelines
- you need maintained, tested software rather than educational code snippets

## Facets
- artifact type: learning-resource
- maturity: active
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, deep-learning, tutorials
- platform: python
- tags: text-classification, survey, feature-extraction, document-classification, educational, natural-language-processing

## Member repositories
- kk7nc/Text_Classification (main) score 39

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:40.890213+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:19:59.034894+00:00, confidence not recorded.
  - readme: https://github.com/kk7nc/Text_Classification (fetched 2026-08-28T04:05:40.890213+00:00, sha 1c61c91ac167)
- Data as of 2026-08-30T08:39:29.467469+00:00.