Skip to main content

Sentiment Analysis

  • Chapter
  • First Online:
Text Analytics with Python

Abstract

In this chapter, we cover one of the most interesting and widely used aspects pertaining to natural language processing (NLP), text analytics, and machine learning. The problem at hand is sentiment analysis or opinion mining, where we want to analyze some textual documents and predict their sentiment or opinion based on the content of these documents. Sentiment analysis is perhaps one of the most popular applications of natural language processing and text analytics, with a vast number of websites, books, and tutorials on this subject. Sentiment analysis seems to work best on subjective text, where people express opinions, feelings, and their mood. From a real-world industry standpoint, sentiment analysis is widely used to analyze corporate surveys, feedback surveys, social media data, and reviews for movies, places, commodities, and many more. The idea is to analyze the reactions of people about a specific entity and take insightful actions based on their sentiments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 44.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Dipanjan Sarkar

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sarkar, D. (2019). Sentiment Analysis. In: Text Analytics with Python. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4354-1_9

Download citation

Publish with us

Policies and ethics