Have you ever wondered why big companies collect user feedback? Obviously, they collect feedback to analyze the user sentiments towards their products or services. It is the only way to know how users are reacting and how to improve the quality of the products or services. Analyzing millions of product reviews manually is impossible and so they use automated data-driven systems to retrieve user sentiments. In this course, Sentiment Classification with Recurrent Neural Networks, you’ll learn how to build a sentiment classifier using recurrent neural networks (RNNs) from scratch using Python and Keras. First, you’ll learn the internal details of recurrent neural networks and how they handle text data effectively. Next, you’ll discover how RNNs can be used to build the network architectures for various natural language processing tasks and specifically, the task of sentiment classification. Then, you’ll work on an open-source email dataset and implement a spam classifier using RNNs. Finally, you’ll explore an open-source dataset of Amazon product reviews and build a system for sentiment classification using RNNs. By the end of this course, you’ll have an in-depth knowledge of sentiment classification systems and you’ll also be capable of implementing one such system using Python and Keras.
Author Name: Biswanath Halder
Author Description:
Biswanath is a Data Scientist who has around nine years of working experience in companies like Oracle, Microsoft, and Adobe. He has extensive knowledge of Machine Learning, Deep Learning, and Reinforcement Learning. He specializes in applying Machine Learning and Deep Learning techniques in complex business applications related to computer vision and natural language processing. He is also a freelance educator and teaches Statistics, Mathematics, and Machine Learning. He holds a Master’s degre… more

Table of Contents

• Course Overview
  1min
• Introduction to Recurrent Neural Networks (RNNs)
  30mins
• Classification of Emails Using RNNs
  26mins
• Sentiment Classification of Product Reviews
  33mins