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Sentiment Analysis with Recurrent Neural Networks in TensorFlow

by Janani Ravi

Recurrent neural networks (RNNs) are ideal for considering sequences of data. In this course, you'll explore how word embeddings are used for sentiment analysis using neural networks.

What you'll learn

Sentiment analysis and natural language processing are common problems to solve using machine learning techniques. Having accurate and good answers to questions without trudging through reviews requires the application of deep learning techniques such as neural networks. In this course, Sentiment Analysis with Recurrent Neural Networks in TensorFlow, you'll learn how to utilize recurrent neural networks (RNNs) to classify movie reviews based on sentiment. First, you'll discover how to generate word embeddings using the skip-gram method in the word2vec model, and see how this neural network can be optimized by using a special loss function, the noise contrastive estimator. Next, you'll delve into understanding RNNs and how to implement an RNN to classify movie reviews, and compare and contrast the neural network implementation with a standard machine learning model, the Naive Bayes algorithm. Finally, you'll learn how to implement the same RNN but with pre-built word embeddings. By the end of this course, you'll be able to understand and implement word embedding algorithms to generate numeric representations of text, and know how to build a basic classification model with RNNs using these word embeddings.

Table of contents

About the author

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more

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