A deep learning model to detect sarcasm in plain text.

Dependencies:

1. Anaconda 4.3.1*
2. Python 3.5.x
3. TextBlob 0.12.0
4. Tensorflow 1.0.1**
5. Scikit-learn 0.18.1
6. Scipy 0.18.1
7. Numpy 1.12.1
8. Nltk 3.2.2

There are 4 files in the project:

1. create_feature_sets.py
2. train_and_test.py
3. exp_replace.py
4. Use_NN.py

There are two dataset files in the project:

1. negproc.npy
2. posproc.npy

Feature-sets are stored in featuresets.npy The model is stored inside folder /model/

Run create_feature_sets.py to extract features from the two dataset files and get featuresets.npy file.

Run train_and_test.py file after the create_feature_sets.py to use the featuresets.npy just created and train the neural network. After train_and_test.py is finished, the model will be saved inside /model/ and can be accessed from there.

exp_replace.py is used by create_feature_sets.py to preprocess the data.

Use_NN.py can be used after we have model saved inside /model/ to use the neural network to make predictions. The input sentence needs to be supplied as a method argument to ‘use_neural_network()’ at the end of the file.

Visualization: To get visualization in Tensorboard, do the following steps:

1. After running train_and_test.py, the logs are collected in /tmp/logs/. Tensorflow uses these logs to generate the visualization.
2. Go to terminal, make sure the location is same as the project location. Run the following command there: tensorboard --logdir=/tmp/logs
3. As part of the output, a URL is provided. The visualization could be accessed by navigating to that URL.

*Install Anaconda: https://docs.continuum.io/anaconda/install **Install Tensorflow: https://www.tensorflow.org/install