Book Image

NLTK Essentials

By : Nitin Hardeniya
Book Image

NLTK Essentials

By: Nitin Hardeniya

Overview of this book

<p>Natural Language Processing (NLP) is the field of artificial intelligence and computational linguistics that deals with the interactions between computers and human languages. With the instances of human-computer interaction increasing, it’s becoming imperative for computers to comprehend all major natural languages. Natural Language Toolkit (NLTK) is one such powerful and robust tool.</p> <p>You start with an introduction to get the gist of how to build systems around NLP. We then move on to explore data science-related tasks, following which you will learn how to create a customized tokenizer and parser from scratch. Throughout, we delve into the essential concepts of NLP while gaining practical insights into various open source tools and libraries available in Python for NLP. You will then learn how to analyze social media sites to discover trending topics and perform sentiment analysis. Finally, you will see tools which will help you deal with large scale text.</p> <p>By the end of this book, you will be confident about NLP and data science concepts and know how to apply them in your day-to-day work.</p>
Table of Contents (17 chapters)
NLTK Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

matplotlib


matplotlib is a very popular visualization library written in Python. We will cover some of the most commonly used visualizations. Let's start by importing the library:

>>>import matplotlib
>>>import matplotlib.pyplot as plt
>>>import numpy

We will use the same running data set from the Dow Jones index for some of the visualizations now. We already have stock data for company "AA". Let's make one more frame for a new company CSCO, and plot some of these:

>>>stockCSCO = stockdata_new.query('stock=="CSCO"')
>>>stockCSCO.head()
>>>from matplotlib import figure
>>>plt.figure()
>>>plt.scatter(stockdata_new.index.date,stockdata_new.volume)
>>>plt.xlabel('day') # added the name of the x axis
>>>plt.ylabel('stock close value') # add label to y-axis
>>>plt.title('title') # add the title to your graph
>>>plt.savefig("matplot1.jpg")  # savefig in local

You can also save the figure...