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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

Extracting Sentiments from Product Reviews

Deciphering the emotional tone behind a sequence of words finds extensive utility in analyzing survey responses, customer feedback, or product reviews. In particular, the advent of social networks offered new possibilities for people to instantly express their opinions on various issues. Therefore, it is not surprising that many shareholders—such as companies, academia, or government—aim to exploit public opinion on various topics and acquire valuable insight.

This chapter focuses on another typical problem in natural language processing (NLP): the extraction of sentiment from a piece of text. For this reason, we incorporate an open source dataset with customer reviews from the Amazon online store. Exploratory Data Analysis (EDA) is again the first task in the pipeline, which helps us discuss important findings on the input data. During this phase, we create different visualizations and enhance our plot construction skills...

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