IMDB Reviews Sentiment Analysis

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
3. Code Usage
   • Regression
   • Classification
4. License
5. Contact
6. Reference

About The Project

• The task of classifying sentiments of texts (for example movie or product reviews) has high practical significance in online marketing as well as financial prediction. This is a non-trivial task, since the concept of sentiment is not easily captured.
• As part of the model building five different approaches where used and please go through the notebooks for more understanding.

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Built With

• Python
  • Data science packages
  • tensorflow
• Google Colab - (Please use colab as it has already most of the packages installed)

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Getting Started

• Code development is performed in the Google colab notebooks. Colab already has required data science packages preinstalled and provided the pip (python package installer) commands where it is required.

• For few data I/O operations like saving the processed files and models, I have used my personal drive.

Prerequisites

• The user should have an google account and access to the google drive as well.
• Kindly install the packages stated in the notebooks.

Code Usage

All the Approaches are summarized in 2 notebooks.

• 1_Multi_models_IMDB_Sentiment_classification.ipynb

  1. TFIDF + classical statistical model
     • Random Forest
     • Naive Bias
  2. LSTM classification model
     • Untuned
     • Tuning using the Keras-Tuning
  3. LSTM model, where the embeddings are initialized with pre-trained word vectors
     • GLOVE Embedding - Wiki 2014 dump
     • GLOVE Embedding - Twitter Data
  4. fastText model - From facebook

• 2_Transformer_Bert_IMDB_Sentiment_classification.ipynb

  • In this script, BERT pretained transformer has been used for training.

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License

Distributed under the GNU License. See LICENSE.txt for more information.

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Contact

1. VS Chaitanya Madduri | LinkedIn

Project Link: https://github.com/chaitanya2593/IMDB_Sentiment_Analysis.git

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Reference

• LSTM - https://slundberg.github.io/shap/notebooks/deep_explainer/Keras%20LSTM%20for%20IMDB%20Sentiment%20Classification.html
• LSTM + GLOVE - https://www.kaggle.com/code/samarthsarin/simple-guide-for-lstm-and-glove-embeddings/notebook
• Transformer Bert - https://www.analyticsvidhya.com/blog/2021/12/fine-tune-bert-model-for-sentiment-analysis-in-google-colab
• Keras-Tuner - https://medium.com/analytics-vidhya/hypertuning-a-lstm-with-keras-tuner-to-forecast-solar-irradiance-7da7577e96eb

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