NLTK
- It is a leading platform for building Python programs to work with human
language data. It provides easy-to-use interfaces to over 50 corpora and
lexical resources such as WordNet, along with a suite of text processing
libraries for classification, tokenization, stemming, tagging, parsing, and
semantic reasoning, wrappers for industrial-strength NLP libraries, and an
active discussion forum.
TextBlob
- It is a Python (2 and 3) library for processing textual data. It provides a
simple API for diving into common natural language processing (NLP) tasks such
as part-of-speech tagging, noun phrase extraction, sentiment analysis,
classification, translation, and more.
Stanford
CoreNLP - It provides a set of human
language technology tools. It can give the base forms of words, their parts of
speech, whether they are names of companies, people, etc., normalize dates,
times, and numeric quantities, mark up the structure of sentences in terms of
phrases and syntactic dependencies, indicate which noun phrases refer to the
same entities, indicate sentiment, extract particular or open-class relations
between entity mentions
spaCy – The spaCy excels at
large-scale information extraction tasks. It's written from the ground up in
carefully memory-managed Cython. spaCy is the best way to prepare text for deep
learning. It interoperates seamlessly with TensorFlow, PyTorch, scikit-learn,
Gensim and the rest of Python's awesome AI ecosystem.
Gensim started off as a collection of Scalable statistical semantics and can
analyze plain-text documents for semantic structure, retrieve semantically similar
documents
Polyglot
is a natural language pipeline that supports massive multilingual applications.