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Advanced Elasticsearch 7.0

You're reading from   Advanced Elasticsearch 7.0 A practical guide to designing, indexing, and querying advanced distributed search engines

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789957754
Length 560 pages
Edition 1st Edition
Languages
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Author (1):
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Wai Tak Wong Wai Tak Wong
Author Profile Icon Wai Tak Wong
Wai Tak Wong
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Toc

Table of Contents (25) Chapters Close

Preface 1. Section 1: Fundamentals and Core APIs FREE CHAPTER
2. Overview of Elasticsearch 7 3. Index APIs 4. Document APIs 5. Mapping APIs 6. Anatomy of an Analyzer 7. Search APIs 8. Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
9. Modeling Your Data in the Real World 10. Aggregation Frameworks 11. Preprocessing Documents in Ingest Pipelines 12. Using Elasticsearch for Exploratory Data Analysis 13. Section 3: Programming with the Elasticsearch Client
14. Elasticsearch from Java Programming 15. Elasticsearch from Python Programming 16. Section 4: Elastic Stack
17. Using Kibana, Logstash, and Beats 18. Working with Elasticsearch SQL 19. Working with Elasticsearch Analysis Plugins 20. Section 5: Advanced Features
21. Machine Learning with Elasticsearch 22. Spark and Elasticsearch for Real-Time Analytics 23. Building Analytics RESTful Services 24. Other Books You May Enjoy

Sentiment analysis

Sentiment analysis is a research topic that analyzes opinions, attitudes, and emotions expressed in a given text. The methodology is to identify and extract subjective information by using context-mining techniques. The general purpose is to judge whether the potential emotions expressed are positive, negative, or neutral based on the source material. Many techniques, such as natural language processing (NLP), text analysis, computational linguistics, statistics, machine learning, and even biometrics, can be applied to sentiment analysis. So far, most users use Elasticsearch as the data store in sentiment analysis and the subsequent search or metric analysis. The workload for sentiment analysis is taken care of by third-party libraries. The following table introduces the two most commonly used libraries:

Name Programming
language
Description
TextBlob Python...
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