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)

Visualizing topics

In this recipe, we will visualize the LDA topic model that we created in Chapter 6, Topic Modeling. The visualization will allow us to quickly see words that are most relevant to a topic and the distances between topics.

Important note

Please see Chapter 6, Topic Modeling, for how to create the LDA model that we will visualize here.

Getting ready

We will use the pyLDAvis package to create the visualization. To install it, use the following command:

pip install pyldavis

How to do it…

We will load the model we created in Chapter 6, Topic Modeling and then use the pyLDAvis package to create the topic model visualization. The visualization is created using a web server:

  1. Import the necessary packages and functions:
    import gensim
    import pyLDAvis.gensim
  2. Load the dictionary, corpus, and LDA model created in Chapter 6, Topic Modeling:
    dictionary = \
    gensim.corpora.Dictionary.load('Chapter06/gensim/id2word.dict')
    corpus = gensim...