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 chapters, you learned about various extraction methods, such as tokenization, stemming, lemmatization, and stop-word removal, which are used to extract features from unstructured text. We also discussed Bag of Words and Term Frequency-Inverse Document Frequency (TFIDF).

In this chapter, you will learn how to use these extracted features to develop machine learning models. These models are capable of solving real-world problems, such as detecting whether sentiments carried by texts are positive or negative, predicting whether emails are spam or not, and so on. We will also cover concepts such as supervised and unsupervised learning, classifications and regressions, sampling and splitting data, along with evaluating the performance of a model in depth. This chapter also discusses how to load and save these models for future use.