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

Training Sentiment Models

The end product of any sentiment analysis project is a sentiment model. This is an object containing a stored representation of the data on which it was trained. Such a model has the ability to predict sentiment values for text that it has not seen before. To develop a sentiment analysis model, the following steps should be taken:

  1. The document dataset must be split into train and test datasets. The test dataset is normally a fraction of the overall dataset. It is usually between 5% and 40% of the overall dataset, depending on the total number of examples available. If the amount of data is too large, then a smaller test dataset can be used.
  2. Next, the text should be preprocessed by stripping unwanted characters, removing stop words, and performing other common preprocessing steps.
  3. The text should be converted to numeric vector representations in order to extract the features. These representations are used for training machine learning models...