Emotion-Detection-RNN

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Emotion-Detection-RNN

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A bidirectional GRU model to detect discrete emotions in tweets.

// repository documentation

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# Emotion Detection Model

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

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## 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-beta0

### 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:
$python3 handler.py.

## 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}
}

Emotion-Detection-RNN

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