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Natural Language Processing and Computational Linguistics

You're reading from   Natural Language Processing and Computational Linguistics A practical guide to text analysis with Python, Gensim, spaCy, and Keras

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838535
Length 306 pages
Edition 1st Edition
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Author (1):
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Bhargav Srinivasa-Desikan Bhargav Srinivasa-Desikan
Author Profile Icon Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan
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Table of Contents (17) Chapters Close

Preface 1. What is Text Analysis? 2. Python Tips for Text Analysis FREE CHAPTER 3. spaCy's Language Models 4. Gensim – Vectorizing Text and Transformations and n-grams 5. POS-Tagging and Its Applications 6. NER-Tagging and Its Applications 7. Dependency Parsing 8. Topic Models 9. Advanced Topic Modeling 10. Clustering and Classifying Text 11. Similarity Queries and Summarization 12. Word2Vec, Doc2Vec, and Gensim 13. Deep Learning for Text 14. Keras and spaCy for Deep Learning 15. Sentiment Analysis and ChatBots 16. Other Books You May Enjoy

Summary

In this chapter, we discussed two important text analysis problems sentiment analysis and building a chatbot. Sentiment analysis refers to the task of understanding sentiment in the text, and we have seen the various libraries, algorithms, and approaches to perform this task. A crucial part of performing such tasks is gathering data we then saw how to download data from internet sources such as Twitter or Reddit. The final section of the chapter focused on how to build chatbots. We explored it from both a historical and theoretical point of view and explored Python libraries that help us easily build chatbots. This brings us to the end of the book you would now be confident in analyzing text the way you see fit, with a variety of techniques, approaches, and settings. We focused on using the most efficient Python open source libraries, with a...

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