Deep Convolutional Neural Network for Sentiment Analysis (Text Classification)
Last Updated on September 3, 2020
Develop a Deep Learning Model to Automatically Classify Movie Reviews
as Positive or Negative in Python with Keras, Step-by-Step.
Word embeddings are a technique for representing text where different words with similar meaning have a similar real-valued vector representation.
They are a key breakthrough that has led to great performance of neural network models on a suite of challenging natural language processing problems.
In this tutorial, you will discover how to develop word embedding models for neural networks to classify movie reviews.
After completing this tutorial, you will know:
- How to prepare movie review text data for classification with deep learning methods.
- How to learn a word embedding as part of fitting a deep learning model.
- How to learn a standalone word embedding and how to use a pre-trained embedding in a neural network model.
Kick-start your project with my new book Deep Learning for Natural Language Processing, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.
- Update Nov/2019: Fixed code typo when preparing training dataset (thanks HSA).
- Update Aug/2020: Updated link to movie review dataset.