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

Introduction

In the previous chapter, we looked at text generation, paraphrasing, and summarization, all of which can be immensely useful in helping us focus on only the essential and meaningful parts of the text corpus. This, in turn, helps us to further refine the results of our NLP project. In this chapter, we will look at sentiment analysis, which, as the name suggests, is the area of NLP that involves teaching computers how to identify the sentiment behind written content or parsed audio—that is, audio converted to text. Adding this ability to automatically detect sentiment in large volumes of text and speech opens new possibilities for us to write useful software.

In sentiment analysis, we try to build models that detect how people feel. This starts with determining what kind of feeling we want to detect. Our application may attempt to determine the level of human emotion (most often, whether a person is sad or happy; satisfied or dissatisfied; or interested or disinterested...