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)

Topic modeling of short texts

In this recipe, we will be using Yelp reviews. These are from the same dataset that we used in Chapter 3, Representing Text: Capturing Semantics. We will break the reviews down into sentences and cluster them using the gsdmm package. The resulting clusters should be about similar aspects and experience, and while many reviews are about restaurants, there are also other reviews, such as those concerning nail salon ratings.

Getting ready

To install the gsdmm package, you will need to create a new folder and then either download the zipped code from GitHub (https://github.com/rwalk/gsdmm) or clone it into the created folder using the following command:

git clone https://github.com/rwalk/gsdmm.git

Then, run the setup script in the folder you installed the package in:

python setup.py install

How to do it…

In this recipe, we will load the data, divide it into sentences, preprocess it, and then use the gsdmm model to cluster the sentences...