This project aims to perform a Sarcasm analysis based on Deep learning text embeddings computed from a large set of headlines from News. To do so, we have employed the NLP Library named as Flair to both extract powerful text embeddings and train a binary classifier to distinguish News titles as sarcastic (positive class label) or non-sarcastic (negative class label).

Moreover, we have implemented a simple user interface in order to make easier the visualzation of the results. In this sense, we have used Flask to build the back-end of the application.

In order to train the model, the News Headlines Dataset which contains a large set of balanced sarcasm/Non-sarcasm headlines. The Dataset can be downloaded from this Kaggle link.

We have included the dataset in both .csv and json format in the /resources folder.

Since the models are very large files, you need to download the model files from this link and uncompress the models.zip file. This will generate a directory such as:

.
├── models
    ├── sarcasm                 # Main Folder
    │   ├── best-model.pt       # Best model during training
    │   ├── final-model.pt      # Final trained model
    │   ├── loss.tsv            # Loss function values during training
    │   ├── test.tsv            # Test results during training
    │   ├── training.txt        # Training Description
    │   ├── weights.txt         # Model weights

The original dataset was split into three different subsets: train, test and dev. During the training process, the model is validated using the testing set. Once the training process ends, we evalute the performance of the best model using the remaining dev subset. The following table shows such performance results:

All the requirements needed to run this project are included in the requirements.txt file. To make sure that you have all the dependencies, we recommend you to execute the following commands:

• Create a new Python environment i.e using Anaconda

conda create -n YOUR_ENV_NAME python=3.6

• Activate your environment

conda activate YOUR_ENV_NAME

• Clone or download the project
• Go to the parent directory of the project
• Install the required packages using the following command

pip install -r requirements.txt

• Configure the port and the host as you wish by creating two environment variables:

  • API_HOST and API_PORT. By default, the host of the application is localhost and the port is 5000.

• Start the service using the following command

python app.py

• Go to http://your_host:your_port and enjoy!

• Main Page of the interface

• Sarcasm Detector Result