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)

TextBlob

TextBlob is a Python library used for NLP. It has a simple API and is probably the easiest way to begin with sentiment analysis and other text analytic areas in Python. TextBlob is built on top of the NLTK library but is a bit easier to use. In the following sections, we will perform an exercise and an activity to get a better understanding of how TextBlob is used in sentiment analysis.

Exercise 63: Basic Sentiment Analysis Using the TextBlob Library

In this exercise, we will perform sentiment analysis on given text. For this, we will be using the TextBlob class of the textblob library. Follow these steps to implement this exercise:

  1. Open a Jupyter notebook.
  2. Insert a new cell and add the following code to implement to import the TextBlob class from the textblob library:
    from textblob import TextBlob
  3. Create a variable named sentence and assign it a string. Insert a new cell and add the following code to implement this:
    sentence = "but you are Late Flight...