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Movie Review Sentiment Prediction

In this project, we’ll develop a deep learning based model to classify the movie reviews into two separate given classes 0[negative] and 1[positive]. In this project, we’ll be using IMDB open-source dataset to build the binary classifiers. In this project, we’ll use sequence-based deep learning models and learn how to improve the model prediction using different approaches.

What will you Learn in the Project?

  1. Reading data from different text files and loading it into the dataframe
  2. Convert text data into vector format for training the deep learning models
  3. Build and train sequence-based model
  4. How to improve on the simple RNN-based model


  1. Working knowledge of Keras library
  2. Theoretical understanding of sequence-based models i.e. RNN, LSTM, GRU

Tools Used

  1. Google colab [Jupyter notebook] for model building 
  2. nltk library 
  3. Keras library for implementing sequence models

Tasks Performed

We will be performing the following tasks as part of this project:

Task-1: Import the various libraries and load the dataset into dataframe

Task-2: Convert text into numerical form for model building.

Task-3: Build the base RNN model for training.

Task-4: Train your model using split train and test data.

Task-5: Build an LSTM model and evaluate it on the test set.

Task-6: Build a Bi-directional LSTM model and evaluate on the test set. 

Task-7: Build a GRU model and evaluate it on the test set.

Task-8: Compare the performance of the above models on a test set and state the best one.

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Skills you will develop

Develop deep learning models for text data

Developing RNN, and LSTM models using Keras

How to implement bi-directional sequence-based model in Keras

Reading the large number of text files and preparing dataframe

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