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text-analytics-with-python - Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer

  •    Python

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

CoreNLP - Stanford CoreNLP: A Java suite of core NLP tools.

  •    Java

Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and 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 word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.

spacy-stanza - 💥 Use the latest Stanza (StanfordNLP) research models directly in spaCy

  •    Python

This package wraps the Stanza (formerly StanfordNLP) library, so you can use Stanford's models in a spaCy pipeline. The Stanford models achieved top accuracy in the CoNLL 2017 and 2018 shared task, which involves tokenization, part-of-speech tagging, morphological analysis, lemmatization and labeled dependency parsing in 68 languages. As of v1.0, Stanza also supports named entity recognition for selected languages. ⚠️ Previous version of this package were available as spacy-stanfordnlp.

Stanford.NLP.NET - Stanford NLP for .NET

  •    F#

Stanford.NLP for .NET is a port of Stanford NLP distributions to .NET. This project contains build scripts that recompile Stanford NLP .jar packages to .NET assemblies using IKVM.NET, tests that help to be sure that recompiled packages are workable and Stanford.NLP for .NET documentation site that hosts samples for all packages. All recompiled packages are available on NuGet.


  •    R

stansent wraps Stanford's coreNLP sentiment tagger in a way that makes the process easier to get set up. The output is designed to look and behave like the objects from the sentimentr package. Plotting and the sentimentr::highlight functionality will work similar to the sentiment/sentiment_by objects from sentimentr. This requires less learning to work between the two packages. In addition to sentimentr and stansent, Matthew Jocker's has created the syuzhet package that utilizes dictionary lookups for the Bing, NRC, and Afinn methods. Similarly, Subhasree Bose has contributed RSentiment which utilizes dictionary lookup that atempts to address negation and sarcasm. Click here for a comparison between stansent, sentimentr, syuzhet, and RSentiment. Note the accuracy and run times of the packages.

Stanford.NLP.Fsharp - F# extentions for The Stanford.NLP.NET

  •    F#

F# wrappers for The Stanford NLP Software is available on NuGet. All these software distributions are open source, licensed under the GNU General Public License (v2 or later). Note that this is the full GPL, which allows many free uses, but does not allow its incorporation into any type of distributed proprietary software, even in part or in translation. Commercial licensing is also available; please contact The Stanford Natural Language Processing Group if you are interested.

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