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

Developing a Text Classifier

A text classifier is a machine learning model that is capable of labeling texts based on their content. For instance, a text classifier will help you understand whether a random text statement is sarcastic or not. Presently, text classifiers are gaining importance as manually classifying huge amounts of text data is impossible. In the next few sections, we will learn about the different parts of text classifiers and implement them in Python.

Feature Extraction

When dealing with text data, features denote its different attributes. Generally, they are numeric representations of the text. As we discussed in Chapter 2, Feature Extraction Methods, TFIDF representations of texts are one of the most popular ways of extracting features from them.

Feature Engineering

Feature engineering is the art of extracting new features from existing ones. Extracting novel features, which tend to capture variation in data better, requires sound domain expertise.

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