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Emotion Detection Model

This is the code for training an emotion detection model using GRU presented in:

GRU Model

Train your own model

Requirements (tested with):

  • Python 3.6
  • Numpy 1.14.5
  • Pandas 0.24.1
  • Sklearn 0.19.0
  • Tensorflow 2.0.0-beta1

Installation:

pip install -r requirements.txt

To run:

After downloading/cloning, put the dataset in the data folder.

To use the dataset in the paper you can download tweets based on their tweet ids available with their classes in ./data/ and remove the hashtags at the end of each tweets. The final dataset should have the following format: id, text, emotion with one record (tweet) per line.

The embedding file should be placed in ./vectorss/

Use the configuration.cfg to set the name of dataset and embedding file, maximun numer in the vocabulary (max_features), maximum length of terms in the text (maxlen), bactch size and number of epochs to run the training.

Then run the handler.py:

python handler.py

Google-Colab

You can use handler.ipynb in order to use in google-colab or jupyter notebook.

Open In Colab

Testing

You can download the trained models used for the paper at: https://drive.google.com/open?id=1TXEbHMTA_AWPFC8bbt7WiBtfT3jVy8cG .

To run, put the test file into the data forlder. test file should be one tweet per line with no additional columns. set the name of the file in test_configuration.cfg and run handler-test.py.

Citation

Please use the following citation when using the code or the paper:

@article{seyeditabari2019emotion, title={Emotion Detection in Text: Focusing on Latent Representation}, author={Seyeditabari, Armin and Tabari, Narges and Gholizadeh, Shafie and Zadrozny, Wlodek}, journal={arXiv preprint arXiv:1907.09369}, year={2019} }