Book Image

Hands-On Python Natural Language Processing

By : Aman Kedia, Mayank Rasu
4 (1)
Book Image

Hands-On Python Natural Language Processing

4 (1)
By: Aman Kedia, Mayank Rasu

Overview of this book

Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding. This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you’ll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own. By the end of this NLP book, you’ll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.
Table of Contents (16 chapters)
1
Section 1: Introduction
4
Section 2: Natural Language Representation and Mathematics
9
Section 3: NLP and Learning

Web scraping libraries and methodology

While discussing NLTK, we highlighted the significance of a corpus or large repository of text for NLP research. While the available corpora are quite useful, NLP researchers may require the text of a particular subject. For example, someone trying to build a sentiment analyzer for financial markets may not find the available corpus (presidential speeches, movie reviews, and so on) particularly useful. Consequently, NLP researchers may have to get data from other sources. Web scraping is an extremely useful tool in this regard as it lets users retrieve information from web sources programmatically.

Before we start discussing web scraping, we wish to underscore the importance of complying with the respective website policies on web scraping. Most websites allow web scraping for individual non-commercial use, but you must always confirm the policy before scraping a website.

To perform web scraping, we will be using a test website (https://webscraper...