Deep Learning with Python and Keras: Build a Model for Sentiment Analysis

Course details

Learn how to apply sentiment analysis to your problems through a practical, real-world use case. In this course, certified Google cloud architect and data engineer Janani Ravi guides you through the process of building and training neural networks to perform sentiment analysis, including validating your results. Go over how to preprocess text for sentiment analysis, as well as approaches you can use and challenges you may encounter. Get set up with Google Colab and import Python modules and loading data, then learn how to analyze word lengths, clean and preprocess text, and visualize text with word clouds. Explore feed-forward neural networks, then dive into configuring, training, and evaluating your dense neural network (DNN). By the end of this course, you’ll also be prepared to train long short-term memory (LSTM) networks.

Concepts

Introduction

An overview of sentiment analysis
Prerequisites

Overview of Sentiment Analysis

Preprocessing text for sentiment analysis
Word vector encodings and word embeddings
Types of sentiment analysis
Approaches and challenges in sentiment analysis

Cleaning and Preprocessing Text Data

Getting setup with google colab
Importing python modules and loading data
Analyzing word lengths across sentiment categories
Cleaning and preprocessing text
Visualizing text using word clouds

Sentiment Analysis Using Dense Neural Networks

Feed forward neural networks
Splitting data into training test and validation sets
Representing text using count vectorization
Configuring the dense neural network
Training and evaluating the DNN
Configuring the count vectorizer as a model layer
Representing text using TF-IDF vectorization
Training and evaluating the model
Representing text using integer sequences
Training a DNN using embeddings

Sentiment Analysis Using Recurrent Neural Networks

Recurrent neural networks
Long memory cells
The LSTM and GRU cells
Training a recurrent neural network
Training an LSTM network
Serializing a model to disk and loading model

Conclusion

Summary and next steps

Deep Learning with Python and Keras: Build a Model for Sentiment Analysis

Authors

Janani Ravi