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)

Clustering sentences using K-means – unsupervised text classification

In this recipe, we will use the same data as in the previous chapter and use the unsupervised K-means algorithm to sort data. After you have read this recipe, you will be able to create your own unsupervised clustering model that will sort data into several classes. You can then later apply it to any text data without having to first label it.

Getting ready

We will use the packages from the previous recipes, as well as the pandas package. Install it using pip:

pip install pandas

How to do it…

In this recipe, we will preprocess the data, vectorize it, and then cluster it using K-means. Since there are usually no right answers for unsupervised modeling, evaluating the models is more difficult, but we will be able to look at some statistics, as well as the most common words in all the clusters.

Your steps are as follows:

  1. Import the necessary functions and packages:
    import nltk...