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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

Sentiment analysis

Sentiment analysis is merely another term given to text classification or document classification where the classifying feature happens to be the sentiment of the text. We can understand sentiment as a feeling or opinion about something if we said The movie was terrific!, it means it expresses a positive sentiment or feeling, and if we say The movie is terrible!, it would be expressing negative sentiment or feeling. Here, sentiment usually refers to positive or negative sentiment, but this can, of course, be extended to include multiple sentiments, such as angry, sad, happy, and maybe even a thoughtful sentiment if we so wish. In other words, sentiment analysis tasks are simply classification tasks where each class is a kind of sentiment which we wish to analyze.

In fact, we have seen an example of sentiment analysis in the previous chapter...

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