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)

Understanding Data for Sentiment Analysis

Sentiment analysis is a type of text classification. Sentiment analysis models are usually trained using supervised datasets. Supervised datasets are a kind of dataset that are labeled with the target variable – usually a column that specifies the sentiment value in the text. This is the value we want to predict in the unseen text.

Exercise 64: Loading Data for Sentiment Analysis

In this exercise, we will load data that could be used to train a sentiment analysis model. For this exercise, we will be using three datasets, namely Amazon, Yelp, and IMDB. Follow these steps to implement this exercise:

  1. Open a Jupyter notebook.
  2. Insert a new cell and add the following code to import the necessary libraries:
    import pandas as pd
    pd.set_option('display.max_colwidth', 200)

    This imports the pandas library. It also sets the display width to 200 characters so that more of the review text is displayed on the screen.

  3. ...