This project aims to study the sentiment analysis using Deep Neural Networks (CNNs and RNNs). During process, we will:
- Process text data.
- Explaine the importance of word embeddings and the powerful idea of embedding stuff.
- Experiment public available word embeddings (GloVe and Word2Vec).
- Use Convolutional Neural Networks to train a sentiment classification model which is better than state-of-the-art resutls of other methods such as Latent Semantic Analysis.
- Experiment Recurrent Neural Networks, more specifically, the long short term memory networks (LSTM).
A very detailed explaination along with accompanying readable code is available in the notebook.
- Python 2.7 and dependencies: numpy, pandas, bcolz, pickle, json, nltk, Theano or Tensorflow, Keras, re
programmed and documented in Jupyter notebook.
- Train on a GPU Testla K80 4x Server or, equivalently, Amazon Web Service EC2 instance (p2.xlarge).