A bidirectional GRU model to detect discrete emotions in tweets.
This is the code for training an emotion detection model using GRU presented in:
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Requirements (tested with):
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.
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.
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}
}