Introduction
In this article I break down the most famous and used Natural Language Processing libraries by ranking them based on GitHub star ratings by developers, describing their use cases and providing tutorial link.
Each library has specific strengths; certain libraries specialise for certain aspects of text analysis.
sPacy has a great tutorial and ease of use is there but misses out on certain aspect of cleaning data, which can be filled with NLTK (Natural Language Toolkit) . TextBlob shines when it comes to sentiment analysis but not when it comes to named entity recognition (NER).
They each have specific strengths and weaknesses and they could have distinct use cases.
I personally have worked with NLTK, sPacy, CoreNLP, TextBlob and fastText and can vouch for each of those libraries. I have worked on cleaning large datasets and I can vouch for going for a hybrid approach.
If you are working with REGEX and if you are doing text analytics, I would say first step is to be a professional with REGEX as it helps a lot in cleaning data. In the article I share best tutorial links for REGEX along with other Libraries.
1. Natural Language Toolkit (NLTK)
Description
One of the best frameworks for Natural language processing tasks with Python that can communicate with natural language data is NLTK. It offers a helpful introduction to language processing programming.
Use Cases:
Many text-processing libraries are included in NLTK for tasks including separating sentences, chunking, lemmatizing, reducing words to their stems, evaluating grammar, and labelling parts of speech.
Tutorial:
NLTK provides you with a free book and runs through all the aspects
2. Gensim
The Python-based Gensim package focuses on topic modelling, document indexing, and finding related information from bigger corpora of work.
All of its techniques can be employed without any memory restrictions, no matter the size of the corpus. It has user-friendly features and can run popular algorithms like LSA/LSI/SVD, LDA, RP, HDP, and word2vec on many cores.
3. CoreNLP
A Stanford group made language processing tools called CoreNLP making it simple and practical to apply linguistic analysis to any text. With just a few lines of code, this collection of tools enables the rapid extraction of features like named-entity recognition and part-of-speech tagging, among others.
CoreNLP is a Java-based NLP system that makes use of a number of Stanford’s NLP technologies, including the named entity recognition (NER), part-of-speech labelling, sentiment analysis, bootstrapped pattern learning, and coreference resolution. Arabic, Chinese, German, French, and Spanish are five more languages that CoreNLP supports in addition to English.
CoreNLP is pretty straight-forward to use and I would recommend starting of this tutorial which tells you how to go about it step by step.
4. spaCy
SpaCy is an open-source Natural Language Processing (NLP) library built on Python. It has been designed specifically to make it possible to develop programmes that handle and comprehend large quantities of language.
SpaCy is renowned for its lightning-fast performance, parsing, identification of named items, tagging using convolutional neural networks, and deep learning integration. There is a fantastic and quick lesson accessible.
Spacy has a great tutorial and I do recommend going through the tutorial, if you are not familiar with it.
5. TextBlob
TextBlob is a programming library built to work with texts in both Python 2 and Python 3. It provides users with a comfortable way to access common text-manipulating functions.
TextBlob is famous for sentiment analysis but text objects can be used as if they were Python strings, with added Natural Language Processing training.
This library also offers a user-friendly API for performing tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, language translation, word inflexion, parsing, n-grams, and integration with WordNet.
The tutorial is pretty straight forward: https://textblob.readthedocs.io/en/dev/quickstart.html
6. Pattern
The pattern is a python library with various sets of features designed to make dealing with data, language, machine learning, and network analysis easier for Python programmers.
It offers a range of tools to facilitate data extraction (such as Google, Twitter, Wikipedia API, a web crawler, and an HTML DOM parser), Natural Language Processing (including part-of-speech taggers, n-gram search, sentiment analysis, and WordNet), Machine Learning (including vector space model, clustering, and SVM), and network analysis by graph centrality and visualization.
It is very powerful as it can help with typing mistakes and/or recognise similar words, it uses various ML technology and it is a must-know for someone who is trying to read through a massive document or text and trying to find similar words.
7. PyNLPl
PyNLPl, which is pronounced “pineapple,” is a natural language processing library. It offers a variety of Python modules that are intended to do tasks related to NLP. Notably, it offers a thorough library for use with FoLiA XML (Format for Linguistic Annotation).
Each of PyNLPl’s different modules and packages can be used for both simple and complex natural language processing projects. PyNLPl has more complex data types and algorithms for more challenging NLP tasks, but it can also be used for simple NLP tasks like extracting n-grams and frequency lists and creating a simple language model.
8. PolyGlot
Polyglot is comparable to spaCy in that it is remarkably efficient, uncomplicated, and an optimal choice for projects that involve a language that spaCy does not cover.
This library is distinguished because it requires the utilization of a particular command line via the pipeline processes. It is worth exploring.
9. scikit-learn
scikit-learn library provides developers with a wide range of algorithms for building machine-learning models. Not only does it contain models for machine learning but also NLP.
It has a great selection of functions to use the bag-of-words approach to address text classification difficulties. The library stands out for its user-friendly classes and methods.
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