Emotion analysis of Tweets using Deep Learning. The goal is to classifify each sample into the corresponding emotion class (Sad, Happy, Angry or Others).
We wrote a short paper describing the system we submited to the competition (pdf version here). Please use the following bibtex entry:
@inproceedings{rebiai-etal-2019-scia,
title = "{SCIA} at {S}em{E}val-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning",
author = "Rebiai, Zinedine and
Andersen, Simon and
Debrenne, Antoine and
Lafargue, Victor",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/S19-2051",
pages = "297--301",
abstract = "In this paper we present our submission for SemEval-2019 Task 3: EmoContext. The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others. Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU). On the leader-board, we submitted two systems that obtained a micro F1 score (F1μ) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1μ of 0.7324 on the test dataset.",
}
Use the requirements.txt file in order to install required libraries.
python run.py
Preprocess datasets, generates embeddings, train both categorical and binary models, ensemble those models and finaly generates a submission file.
You can checkout the playground.ipynb notebook which showcase our final work.
The language_model_playground folder is not meant to be used "as-is", indeed it needs fastai's courses library installed. Instead it's only here to showcase some experiments we did.
Our best model scored 0.7324 micro-f1 on the test set. This model is an ensembling of our best categorical model and our best binary model. You can check results on this page.