You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801071970

Length 620 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Science

Author (1):

Nathan George

View More author details

Table of Contents (30) Chapters

Preface

1. Part I - An Introduction and the Basics

2. Introduction to Data Science FREE CHAPTER

3. Getting Started with Python

4. Part II - Dealing with Data

5. SQL and Built-in File Handling Modules in Python

6. Loading and Wrangling Data with Pandas and NumPy

7. Exploratory Data Analysis and Visualization

8. Data Wrangling Documents and Spreadsheets

9. Web Scraping

10. Part III - Statistics for Data Science

11. Probability, Distributions, and Sampling

12. Statistical Testing for Data Science

13. Part IV - Machine Learning

14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction

15. Machine Learning for Classification

16. Evaluating Machine Learning Classification Models and Sampling for Classification

17. Machine Learning with Regression

18. Optimizing Models and Using AutoML

19. Tree-Based Machine Learning Models

20. Support Vector Machine (SVM) Machine Learning Models

21. Part V - Text Analysis and Reporting

22. Clustering with Machine Learning

23. Working with Text

24. Part VI - Wrapping Up

25. Data Storytelling and Automated Reporting/Dashboarding

26. Ethics and Privacy

27. Staying Up to Date and the Future of Data Science

28. Other Books You May Enjoy

29. Index

Sentiment analysis

One last topic to examine under text analysis is sentiment analysis. This is the measurement of the positive or negative sentiment of text. There are similar analyses that can be done to measure the emotion of text. The way this works is similar to the last section – we create features from our text, then train a classifier to predict either sentiment, emotions, or some other class from a list of labels. With sentiment analysis, we can train a regressor to predict the sentiment if we label our text with values such as -5 ranging to +5, or we can train a classifier to simply predict positive, neutral, or negative.

Another method for determining sentiment of text are rule-based algorithms. These can use lookup dictionaries where words have a sentiment score, and the sentiment score for a text is added up and averaged to get the overall sentiment. Negation rules can be applied, such as the word "not" before another word flips its value.

There...