Book Image

Natural Language Processing Fundamentals

By : Sohom Ghosh, Dwight Gunning
Book Image

Natural Language Processing Fundamentals

By: Sohom Ghosh, Dwight Gunning

Overview of this book

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
Table of Contents (10 chapters)

Introduction

In the previous chapter, we learned about different ways to collect data from local files and online resources. In this chapter, we will focus on topic modeling, which is a popular concept within natural language processing. Topic modeling is a simple way to capture meaning from a collection of documents. Note that, in this case, documents are any coherent collection of words, which could be as short as a tweet or as long as an article, based on the project at hand.

A topic model captures information about the concepts contained in a set of texts. Using these concepts, documents can be organized into different categories or topics.

Topic modeling is mostly done using unsupervised learning algorithms, which detect topics on their own. Topic modeling algorithms operate by doing statistical analysis of words or tokens in documents, and then they use those statistics to automatically assign documents to topics.

In this chapter, we will look at a few popular topic...