Book Image

The Natural Language Processing Workshop

By : Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning
5 (1)
Book Image

The Natural Language Processing Workshop

5 (1)
By: Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning

Overview of this book

Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you’ve never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you’ll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.
Table of Contents (10 chapters)
Preface

Topic Discovery

The main goal of topic modeling is to find a set of topics that can be used to classify a set of documents. These topics are implicit because we do not know what they are beforehand, and they are unnamed.

The number of topics could vary from around 3 to, say, 400 (or even more) topics. Since it is the algorithm that discovers the topics, the number is generally fixed as an input to the algorithm, except in the case of non-parametric models in which the number of topics is inferred from the text. These topics may not always directly correspond to topics that a human would find meaningful. In practice, the number of topics should be much smaller than the number of documents. In general, the number of topics specified in a parametric model ought to be greater than or equal to the expected number of topics in the text. In other words, one should err on the side of a greater number of topics rather than fewer topics. This is because fewer topics can cause a problem for...