Introduction
In this article, I break down the most famous and most 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 however 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 advise on which library is used for which reason. I have worked on cleaning large datasets and I can vouch for going for a hybrid approach.
If you are working with unclean data – I strongly recommend getting better Regex as it helps a lot in cleaning data. In the article I share best tutorial links for NLP libraries along with Regex.,
Natural Language Toolkit (NLTK)
Github Star Ranking
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
Gensim
Github Star Ranking:
Description
The Python-based Gensim package focuses on topic modelling, document indexing, and finding related information from bigger corpora of work.
Use Case
Gensim is a python library for topic modeling and document similarity analysis. It is commonly used for tasks such as latent dirichlet allocation, latent semantic analysis and word2vec training. It is commonly used for text summarisation, document classification and information retrieval. Gensim specialises with it ability to handle with large text collection and can work smoothly with streaming data, they can be live continuos stream of data feed – there is a article on gensim in stock market, which is used to get stock market prices with news headline:
Tutorial
https://radimrehurek.com/gensim/auto_examples/index.html
CoreNLP
Github Star Ranking:
Description
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.
Use Case
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.
Tutorial
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.
spaCy
Github Star Ranking
Description
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.
Use Case
SpaCy mentions on their website industrial grade natural language processing and this is due its lightning-fast performance, parsing, identification of named items, tagging using convolutional neural networks, and deep learning integration. These are the high level use cases
- Text pre-processing: sPacy can perform pre-processing but it is recommended to use NLTK and Regex from experience to do so as that allows a lot of power for preprocessing but Spacy is used to perform tasks such as tokenisation, lemmatisation and part of speech tagging.
- Named Entity Recognition (NER): spaCy can be used to identify and extract entities such as people, organizations, and locations from text.
- Dependency Parsing: spaCy can be used to analyze the grammatical structure of a sentence and to identify the relationships between words.
- Sentiment Analysis: spaCy can be used to determine the sentiment of text, for example, whether a piece of text is positive, negative or neutral.
- Text Classification: spaCy can be used to train machine learning models for text classification tasks such as spam detection, sentiment analysis, and topic classification.
- Information Extraction: spaCy can be used to extract structured information from unstructured text, such as dates, times, and quantities.
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Machine Learning: spaCy can be used as a part of a machine learning pipeline to improve the performance of models by providing features such as part-of-speech tags, dependency parse, and named entities.
Tutorial
Spacy has a great tutorial and I do recommend going through the tutorial, if you are not familiar with it.
TextBlob
Github Star Ranking
Description
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.
Use Cases
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.
From experience Textblob is one of the first for sentiment analysis, other libraries can outshine in certain areas but Textblob shines with sentiment analysis
Tutorial
The tutorial is pretty straight forward: https://textblob.readthedocs.io/en/dev/quickstart.html
Pattern
Github Star Ranking:
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.
Use Case
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.
Tutorial
Pattern Library for Natural Language Processing in Python
PyNLPl
Github Star Ranking
Descriptioon
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).
Use Cases
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.
Tutorial
PolyGlot
Github Star Ranking
Watchers
Description
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.
Use Cases
- Polyglot can be used to identify the language of a given text through language detection.
- Polyglot can be used to recognise and extract named entities (NER), such as individuals, organisations, and locations from text.
- Polyglot can be used to tag words in a statement according to their part of speech.
- Text sentiment, such as whether a passage is favourable, negative, or neutral, can be assessed using Polyglot’s sentiment analysis feature.
- Machine learning models for text classification tasks like spam detection and sentiment analysis can be trained using Polyglot.
- Text translation from one language to another is possible using Polyglot.
- Pre-trained word embeddings are available from Polyglot for a number of languages
Tutorial
WORTHABLE MENTION: scikit-learn
Github Star Ranking
Description
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.
Use Cases
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Text Classification: scikit-learn provides a variety of classifiers such as logistic regression, support vector machines (SVMs), and naive Bayes that can be used to train models for text classification tasks such as spam detection and sentiment analysis.
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Feature Extraction: scikit-learn provides tools for feature extraction such as bag-of-words and tf-idf, which can be used to convert text data into numerical features that can be used by machine learning models.
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Model Evaluation: scikit-learn provides tools for evaluating the performance of machine learning models such as cross-validation and metrics like accuracy, precision, and recall.
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Hyperparameter tuning: scikit-learn provides tools like GridSearchCV, RandomizedSearchCV to find the best hyperparameters for a given model
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Clustering: scikit-learn provides several clustering algorithms like K-means, Affinity Propagation, which can be used to group similar documents together.
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Dimensionality Reduction: scikit-learn provides techniques like LSA, LDA, and NMF, which can be used to reduce the number of features in text data while preserving the most informative ones.
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Text Generation: scikit-learn can be used to train language models using techniques such as Markov Chain and Hidden Markov Model, which can be used for tasks such as text generation and text completion.
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Text summarization: scikit-learn can be used for extractive summarization techniques, where important sentences are extracted from the text to form a summary.
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