AN INTRODUCTION TO WEB MOVIE (IMDb) DATASETS AND NATURAL LANGUAGE PROCESSING

This chapter will introduce IMDb internet movies material, and use word embedding methods for natural language preprocessing, after that build a variety of deep learning model (emotional analyze). Then, I will use:
# Sentiment analysis called opinion probe survey (opinion mining).
# Use "Natural Language Processing", text analysis and other methods to find out of the author's attitude, emotion, evaluation, or sentiment on certain topics.
# The business of sentiment analysis value, can know in advance how customers feel about a company or product perception, with the adjustion of the direction of sales strategy.
# IMDb data set shared 50000 pen "film comment text", divided into training materials with test data 25000 pen, every pen "film review text" is marked for positive review or negative review.
# The following deep learning models identify IMDb "film review text", it can be divided into training and prediction.

The model to be built can be shown below:

Training: IMDb set of training data is 25000 pen, after data preprocessing, the features (Eigen values) and label (1: positive evaluation, 0: negative evaluation), then deep learning model is carried out training, the trained model can be as the next stage forecast usage.
Predict: Enter "Film review text", the pre-processing will produce features eigen value, and its training available to complete the multilayer perceptron model which carried out the forecast, finally it will produce predictions ("Positive" or "Negative").

The steps natural language processing of the film review text are as follows:

FULL CODE OF NATURAL LANGUAGE PROCESSING USING IMDb

After learning the steps, now we can implement the code like below: