Sentiment Classifier for IMDB Review Dataset
This Experiment show all the process done for reach some close SotA on sentiment analysis using IMDB Dataset. From EDA to Model Definition And Fitting. The Dataset was divided in 3 parts, Training(45k examples), Evaluation(2.5k examples) and Test(2.5k examples) sets. The model architecture features LSTM + Dense Networks, slightly regularized and Batch Normalized.
The next link, redirect to Weights And Biases web page, where was logged the experiments results: https://app.wandb.ai/elpapi42/Sentiment-Analysis-Classifier?workspace=user-elpapi42
This link is for an general architecture overview: https://www.plectica.com/maps/NMR0B1AZT
There exist 4 of the most relevant runs of the Model during experiments. proud-tree-81 was the iteration with the best performance on test, Reaching 99.4% Accuracy and minimun variance from Evaluation To Training set: https://app.wandb.ai/elpapi42/Sentiment-Analysis-Classifier/runs/c6o93kl3?workspace=user-elpapi42
If you cant see the notebook due to GitHub issues, check this: https://nbviewer.jupyter.org/github/ElPapi42/SentimentAnalysisClassifier/blob/master/SentimentAnalysisClassifier.ipynb
References:
Original Paper Presenting IMDB Reviews Dataset:
Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher Learning Word Vectors for Sentiment Analysis Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies June 2011 Portland, Oregon, USA Association for Computational Linguistics Pages: 142-150 http://www.aclweb.org/anthology/P11-1015