Sentiment analysis is the capability to decipher the opinions contained in a written or spoken text. The main purpose of this technique is to identify the sentiment (or polarity) of a lexical expression, which may have a neutral, positive, or negative connotation.
The problem we want to resolve is the IMDB movie review sentiment classification problem. Each movie review is a variable sequence of words, and the sentiment (positive or negative) of each movie review must be classified.
This problem is very complex, because the sequences can vary in length; they can also be part of a large vocabulary of input symbols.
The solution requires the model to learn long-term dependencies between symbols in the input sequence.
The IMDB dataset contains 25,000 highly polarized movie reviews (good or bad) for training and the same amount again for testing. The data was collected by Stanford...
