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

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 is 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 8.02: 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.

Note

You can find the data being used in this exercise here: https://packt.live/2XgeQqJ.

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...