Sentiment Analysis using Convolutional Neural Networks with Keras
Sentiment analysis using CNNs built using Keras with a validation accuracy around 79%
- Keras
- Tensorflow
- Numpy
The model is trained on a portion of Twitter's Sentiment Analysis Dataset, you can download it here.
The dataset as a csv file is stored in data folder and is preprocessed and used for training.
The model gets a validation accuracy of around 79% by training on 15,78,627 entries of the Twitter dataset over 5 epochs.
Run the classify.py in your terminal to test the Sentiment Analysis model on any text.
The code for preproccessing is in the preprocess.py and the model along with code to train the model is in model.py
A simple sequential architecture consisting of few Convolutional layers and the Fully Connected Layer.