Python NLP Libraries: Features, Use Cases, Pros and Cons

Author: Tomasz Bąk
5 min read
Python NLP Libraries: Features, Use Cases, Pros and Cons

Text data the most common form of information on the Internet, whether it be reviews, tweets or web pages. Natural Language Processing (NLP) is a powerful technology that helps you derive immense value from that data. In this article, we will look at the most popular Python NLP libraries, their features, use cases, pros, and cons.

TextBlob - Great library for getting started

TextBlob is based on NLTK and Pattern. It has great API for all the common NLP operations. It’s a more practical library concentrated on day-to-day usage.

It's great for initial prototyping in almost every NLP project. Unfortunately, it inherits the low performance from NLTK and therefore it's not good for large scale production usage.

TextBlob features

tokenization, POS, NER, classification, sentiment analysis, spellcheck, parsing, translation and language detection

TextBlob use cases

TextBlob Quickstart example NLP use cases:

  • Sentiment Analysis
  • Spelling Correction
  • Translation and Language Detection

Pros

  • easy to use and intuitive interface to NLTK library
  • provides language translation and detection which is powered by Google Translate

Cons

  • slow
  • no neural network models
  • no integrated word vectors

NLTK - The most famous Python NLP library

We can't talk about NLP in Python without mentioning Natural Language Toolkit (NLTK) is one of the most comprehensive NLP libraries and the most famous Python NLP library.

NLTK is a very powerful tool. It is most popular in education and research. It has led to many breakthroughs in text analysis. It has a lot of pre-trained models and corpora which helps us to analyze things very easily. It is an excellent library when you require a specific combination of algorithms.

The learning curve is steep and most of the time, it’s rather slow and often doesn’t match the demands of real word production usage.

NLTK features

tokenization, POS, NER, classification, sentiment analysis, access to corpora, package for chatbots

NLTK use cases

Pros

  • most well-know and full NLP library with many 3rd extensions
  • supports the largest number of languages compared to other libraries

Cons

  • difficult to learn and use
  • slow
  • only splits text by sentences, without analyzing the semantic structure
  • no neural network models

spaCy - Lightning-fast and Gets Things Done!

spaCy is an advanced NLP library available in Python and Cython. It is geared toward performance and operating together with deep learning frameworks such as TensorFlow or PyTorch.

It comes with pre-trained statistical models and word vectors. It features tokenization for 50+ languages, convolutional neural network models for tagging, parsing and named entity recognition.

spaCy features

tokenization, POS, NER, classification, sentiment analysis, dependency parsing, word vectors

spaCy use cases

  • Search autocomplete (and autocorect) is popular type of NLP that many people use on a daily basis.

  • Analyze online reviews. Extract the key topics covered by the reviews without having to go through all of them. Help the sellers/retailers get consumer feedback in the form of topics (extracted from the consumer reviews).

  • Automatic Summarization of Resumes with NER - Evaluate resumes at a glance to facilitate evaluation of resumes at a quick glance, thereby simplifying the effort required in shortlisting candidates among a pile of resumes.

Pros

  • fast
  • easy to learn and use
  • uses neural networks for training models

Cons

  • less flexibility compared to NLTK

Pattern - All-in-One: data mining, scraping, NLP, ML

Pattern library is a multipurpose library capable of handling NLP, data mining, machine learning, network analysis, and visualization. It comes with modules for data mining from search engines, social networks, and Wikipedia. It also can download and parse PDF documents.

It is one of the most useful NLP libraries in Python. While it is not as well-known as spaCy or NLTK, it provides features such as finding superlatives and comparatives, and fact and opinion detection which it stand out from the other NLP libraries.

Pattern features

tokenization, POS, NER, sentiment analysis, parsing

Pattern use cases

Introduction to the Pattern Library NLP use cases include:

  • Finding Sentiments
  • Spelling Corrections
  • Getting Search Engine Results with APIs
  • Converting HTML Data to Plain Text

Pros

  • data mining web services (Google, Twitter, Wikipedia), web crawler, HTML DOM parser
  • network analysis and visualization.

Cons

  • not optimized for some specific NLP tasks

Summary - Choosing Python NLP Library

With Python’s extensive NLP libraries Python developers can build amazing text processing applications effectively and help their organizations gain valuable insights from text data.

There are many Python NLP libraries that provide specific features. Choosing the best NLP library for your projects or task is all about knowing which features are available and how they compare to each other.