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Natural Language Processing and Computational Linguistics

You're reading from  Natural Language Processing and Computational Linguistics

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838535
Pages 306 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Bhargav Srinivasa-Desikan Bhargav Srinivasa-Desikan
Profile icon Bhargav Srinivasa-Desikan
LinkedIn
Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
See other products by Bhargav Srinivasa-Desikan

Table of Contents (22) Chapters

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
What is Text Analysis? Python Tips for Text Analysis spaCy's Language Models Gensim – Vectorizing Text and Transformations and n-grams POS-Tagging and Its Applications NER-Tagging and Its Applications Dependency Parsing Topic Models Advanced Topic Modeling Clustering and Classifying Text Similarity Queries and Summarization Word2Vec, Doc2Vec, and Gensim Deep Learning for Text Keras and spaCy for Deep Learning Sentiment Analysis and ChatBots Other Books You May Enjoy Index

Visualizing topic models

Like we have said before, the purpose of topic models is to better understand our textual data - and visualizations are one of the best ways to understand and look at our data. There are multiple ways and techniques to visualize topic models - we will be focusing on the methods implemented and compatible with Gensim, but like we have done throughout the book, we will be providing links and documentation to the other popular topic modeling visualization tools.

One of the most popular topic modeling visualization libraries is LDAvis - an R library build largely on D3, it has been ported to Python as pyLDAvis and is just as nifty in Python and is very well integrated with Gensim as well. It is based on the original paper (LDAvis: A method for visualizing and interpreting topics [19]) by Carson Sievert and Kenneth E. Shirley.

The pyLDAvis library is agnostic...

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Authors (1)

Profile icon Bhargav Srinivasa-Desikan
LinkedIn
Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
See other products by Bhargav Srinivasa-Desikan

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