# dipanjanS/text-analytics-with-python

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

Repository: https://github.com/dipanjanS/text-analytics-with-python
Canonical: https://ross.abutalabs.com/products/text-analytics-with-python
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: text-analytics, text-summarization, text-classification, python, natural-language, natural-language-processing, clustering, sentiment, semantic, sentiment-analysis, nltk, stanford-nlp, spacy, pattern, scikit-learn, gensim
Last push: 2020-12-24T15:46:21+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3629, "days_push": 2078, "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 1698, forks 845 (observed 2026-08-28T04:05:24.034758+00:00)

## What it is
Companion repository for the book 'Text Analytics with Python' (2nd Edition, Apress/Springer), containing Jupyter notebooks, code, and datasets covering NLP techniques from text processing to deep learning-based sentiment analysis. It serves as a hands-on learning resource for practitioners studying text analytics with Python.

## Use cases
- learn natural language processing with python
- sentiment analysis tutorial code
- text classification examples in python
- topic modeling with gensim notebooks
- text summarization implementation examples
- learn spacy and nltk through worked examples
- word embeddings and deep learning for NLP

## When to choose
- learning NLP concepts through runnable notebooks
- following a structured book-based curriculum on text analytics
- studying both classical ML and deep learning approaches to text

## When to avoid
- need a production-ready NLP library
- want actively maintained tooling rather than educational code
- need the latest LLM/transformer-era techniques

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, data-science
- domain: machine-learning, tutorials, data-science
- platform: python, cross-platform
- tags: text-analytics, sentiment-analysis, text-classification, text-summarization, topic-modeling, jupyter-notebooks, book-companion, nltk, spacy, gensim, scikit-learn, natural-language-processing

## Member repositories
- dipanjanS/text-analytics-with-python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:24.034758+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 2026-08-30T03:37:49.608365+00:00, confidence not recorded.
  - readme: https://github.com/dipanjanS/text-analytics-with-python (fetched 2026-08-28T04:05:24.034758+00:00, sha c1bea6163c0a)
- Data as of 2026-08-30T08:39:29.467469+00:00.