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Each movie review is a variable sequence of words and the sentiment of each movie review must be classified.

The Large Movie Review Dataset (often referred to as the IMDB dataset) contains 25,000 highly-polar movie reviews (good or bad) for training and the same amount again for testing.

Finally, the sequence length (number of words) in each review varies, so we will constrain each review to be 500 words, truncating long reviews and pad the shorter reviews with zero values.

Now that we have defined our problem and how the data will be prepared and modeled, we are ready to develop an LSTM model to classify the sentiment of movie reviews.

We will also limit the total number of words that we are interested in modeling to the 5000 most frequent words, and zero out the rest.The problem is to determine whether a given movie review has a positive or negative sentiment.The data was collected by Stanford researchers and was used in a 2011 paper where a split of 50-50 of the data was used for training and test. Keras provides access to the IMDB dataset built-in.In this post, you will discover how you can develop LSTM recurrent neural network models for sequence classification problems in Python using the Keras deep learning library.After reading this post you will know: The problem that we will use to demonstrate sequence learning in this tutorial is the IMDB movie review sentiment classification problem.

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