A deep learning model to detect sarcasm in plain text.

• Anaconda 4.3.1*
• Python 3.5.x
• TextBlob 0.12.0
• Tensorflow 1.0.1**
• Scikit-learn 0.18.1
• Scipy 0.18.1
• Numpy 1.12.1
• Nltk 3.2.2

• create_feature_sets.py
• train_and_test.py
• exp_replace.py
• Use_NN.py

• negproc.npy
• 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.

To get visualization in Tensorboard, do the following steps:

• After running train_and_test.py, the logs are collected in /tmp/logs/. Tensorflow uses these logs to generate the visualization.

• Go to terminal, make sure the location is same as the project location. Run the following command there: tensorboard --logdir=/tmp/logs

• 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