Book Image

Python Natural Language Processing Cookbook

By : Zhenya Antić
Book Image

Python Natural Language Processing Cookbook

By: Zhenya Antić

Overview of this book

Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You’ll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you’ll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data. By the end of this NLP book, you’ll have developed the skills to use a powerful set of tools for text processing.
Table of Contents (10 chapters)

Discovering sentiment analysis

In this recipe, we will use two simple tools for labeling a sentence as having positive or negative sentiment. The first tool is the NLTK Vader sentiment analyzer, and the second one uses the textblob package.

Getting ready

We will need the nltk and textblob packages for this recipe. If you haven't already installed them, install them using these commands:

pip install nltk
pip install textblob

In addition to this, you will need to run the following from Python the first time you use the Vader sentiment analyzer:

>> import nltk
>>nltk.download('vader_lexicon')

How to do it…

We will define two functions: one will do sentiment analysis using NLTK, and the other using TextBlob.

Your steps should be formatted like so:

  1. Import the packages:
    from textblob import TextBlob
    from nltk.sentiment.vader import SentimentIntensityAnalyzer
  2. Define the sentences list:
    sentences = ["I love going to school...