---
id: textblob
version: "0.20.1"
license: MIT
license_treatment: permissive
maintenance: active
---

# textblob — Simple, Pythonic text processing. Sentiment analysis, part-of-speech tagging, noun phrase parsing, and more.

License: permissive · Maintenance: active · Downloads: 7.6M/mo

## What it is and what it does

TextBlob is a Python library that wraps NLTK and pattern to provide a simplified interface for common natural language processing tasks. It lets you work with text through an intuitive API, extracting linguistic features like parts of speech, noun phrases, and sentiment polarity from strings without deep NLP expertise.

The library handles tokenization, tagging, phrase extraction, and sentiment scoring out of the box. It also supports text classification, spelling correction, word inflection, and n-gram analysis. You create a TextBlob object from a string and access properties like .tags, .noun_phrases, and .sentences.sentiment to get results. It requires downloading linguistic corpora once after installation.

Use it for:

- Analyze sentiment polarity of customer reviews or social media posts to gauge positive/negative feedback
- Extract key noun phrases from documents for topic identification or keyword extraction
- Tag parts of speech in text for grammatical analysis or downstream NLP pipelines
- Classify text documents using built-in Naive Bayes or Decision Tree classifiers
- Correct spelling errors and normalize text before further processing

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

TextBlob provides a simple API for common natural language processing tasks including sentiment analysis, part-of-speech tagging, noun phrase extraction, tokenization, and text classification.

Yes, if you need straightforward NLP tasks without deep learning complexity. TextBlob is well-maintained, has no security vulnerabilities, and offers a gentle entry point to text processing. Install it for sentiment analysis, phrase extraction, or basic classification. Skip it if you require state-of-the-art accuracy or modern transformer-based models.

## Install

pip install textblob
uv add textblob
poetry add textblob

## Installing textblob

Before you install: Low install friction with a single runtime dependency on nltk. The project is actively maintained with a recent release (27 days old) and a substantial repository history since 2013, indicating stable long-term support.

License in practice: MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects without licensing concerns.

Quickstart:

pip install textblob
python -m textblob.download_corpora

from textblob import TextBlob
blob = TextBlob('The movie was great!')
print(blob.sentiment.polarity)

Requires Python 3.10 or later. The download_corpora step must be run once to fetch required linguistic data before sentiment analysis and other NLP features work.

Verify before relying:

- Performance characteristics on large text volumes or real-time processing scenarios
- Accuracy of sentiment analysis and classification compared to modern transformer-based alternatives

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 7.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

sentiment analysis python, nlp text processing, part-of-speech tagging, noun phrase extraction, text classification, tokenization library, simple nlp api, nlp, sentiment-analysis, text-processing

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