Srikanth Pagadala

Predict Sentiment From Movie Reviews

09 Nov 2016

Sentiment analysis is a natural language processing problem where text is understood and the underlying intent is predicted.

Let us learn how you can predict the sentiment of movie reviews as either positive or negative in Python using the Keras deep learning library.

By the end you will know:

  • About the IMDB sentiment analysis problem for natural language processing and how to load it in Keras.
  • How to use word embedding in Keras for natural language problems?
  • How to develop and evaluate a multi-layer perception model for the IMDB problem?
  • How to develop a one-dimensional convolutional neural network model for the IMDB problem?

IMDB Movie Review Sentiment Problem Description

The dataset is the Large Movie Review Dataset often referred to as the IMDB dataset.

The Large Movie Review Dataset contains 25,000 highly polar moving reviews (good or bad) for training and the same amount again for testing. The problem is to determine whether a given moving review has a positive or negative sentiment.

The data was collected by Stanford researchers where a split of 50/50 of the data was used for training and test. An accuracy of 88.89% was achieved.

The data was also used as the basis for a Kaggle competition titled “Bag of Words Meets Bags of Popcorn” in late 2014 to early 2015. Accuracy was achieved above 97% with winners achieving 99%.

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