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

Summary

In this chapter, you have learned about various types of data and ways to deal with unstructured text data. Text data is usually extremely noisy and needs to be cleaned and preprocessed, which mainly consists of tokenization, stemming, lemmatization, and stop-word removal. After preprocessing, features are extracted from texts using various methods, such as BoW and TFIDF. These methods convert unstructured text data into structured numeric data. New features are created from existing features using a technique called feature engineering. In the last part of this chapter, we explored various ways of visualizing text data, such as word clouds.

In the next chapter, you will learn how to develop machine learning models to classify texts using the feature extraction methods you have learned about in this chapter. Moreover, different sampling techniques and model evaluation parameters will be introduced.