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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Understanding sentiment analysis

You are running for public office, and to increase the chances of being elected, you must perform a substantial effort to persuade the voters. This undertaking becomes even more challenging for non-sympathizers and ambivalent citizens. Hence, a possible strategy is to focus on less favorable regions to your candidacy, which can be identified from the sentiment expressed in social media posts in this area. Similarly, suppose you are the CEO of a company that recently deployed a new product. This time, you are interested in knowing how your customers perceive it and in understanding their opinions. In both scenarios, you should also be concerned about the competition and the sentiment against your opponents’ political campaigns or competitor products. All these issues can be addressed by performing sentiment analysis: assigning a sentiment label to a piece of text. This task is the current chapter’s theme.

Recall the discussion in the...

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