Using Deep Learning Neural Networks to classify reviews of movie dataset to Positive and Negative Sentiment
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Keras-LSTM-Sentiment-classification.ipynb Add files via upload Oct 7, 2017
README.md Update README.md Oct 7, 2017
movie_reviews.csv Add files via upload Oct 7, 2017

README.md

Keras-LSTM-Sentiment-Classification

Using Deep Learning Neural Networks to classify reviews of movie dataset to Positive and Negative Sentiment.

We are using keras that act as a Wrapper on top of Theano/Tensorflow to create ML models easily as creating models using Theano or Tensorflow requires a lot of code to be written.

Requirements -

  1. Python 3
  2. Google word vectors (https://code.google.com/archive/p/word2vec/)
  3. Theano/Tensorflow (I have created model using Theano)
  4. Keras (As a wrapper around Theano/Tensoflow)

Here we have used LSTM that are best RNN for doing text classification. Its a binary class problem i.e positive and Negative sentiment. I was able to get 90% accuracy. But we can improve it more my creating more complex model and tuning the hyper parameters.

Just run Keras-LSTM-Sentiment-classification.ipynb notebook and check the results. Happy Learning.