How to Develop a Deep Learning Bag-of-Words Model for Sentiment Analysis (Text Classification)
Last Updated on September 3, 2020
Movie reviews can be classified as either favorable or not.
The evaluation of movie review text is a classification problem often called sentiment analysis. A popular technique for developing sentiment analysis models is to use a bag-of-words model that transforms documents into vectors where each word in the document is assigned a score.
In this tutorial, you will discover how you can develop a deep learning predictive model using the bag-of-words representation for movie review sentiment classification.
After completing this tutorial, you will know:
- How to prepare the review text data for modeling with a restricted vocabulary.
- How to use the bag-of-words model to prepare train and test data.
- How to develop a multilayer Perceptron bag-of-words model and use it to make predictions on new review text data.
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 Oct/2017: Fixed a minor typo when loading and naming positive and negative reviews (thanks Arthur).
- Update Aug/2020: Updated link to movie review dataset.
To finish reading, please visit source site