Sentiment analysis on movie reviews using LSTM neural networks
Given a movie review classify it as either positive or negative, after training the agent on an adnotated dataset.
For this task i have used the "Large Movie Review Dataset" provided by Stanford University
You can read their paper on this subject here :
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
- numpy
- tensorflow
- spacy - all word embedding vectors were loaded using spacy
If you have not yet downloaded the dataset mentioned above, the script will attempt to download it for you on the first run.
First you should run train_LSTM.py. This script will preprocess the dataset and save it in a separate folder. Afterwards it will begin training the LSTM neural network. To visualize the progress in Tensorboard you can run the following command tensorboard --logdir=logs
After training the network you can run the script test_LSTM.py to see how well the network fits the test set.
The most exciting script by far is own_input_test_LSTM.py where you can enter a review as a string and see how the network classifies it.