[PDF][PDF] Emotion Detection for Spanish by Combining LASER Embeddings, Topic Information, and Offense Features.
F Vitiugin, G Barnabo - IberLEF@ SEPLN, 2021 - researchgate.net
IberLEF@ SEPLN, 2021•researchgate.net
This paper describes the system submitted by WSSC Team to the EmoEvalEs@ IberLEF
2021 emotions detection competition. We propose a novel model for Emotion Detection that
combines transformers embeddings with topic information and offense features. The system
classifies social media text emotions leveraging its context representations. Our results show
that, for this kind of task, our model outperforms baselines and state-of-the-art text
classification methods. As for the leader-board, our classification model achieved a macro …
2021 emotions detection competition. We propose a novel model for Emotion Detection that
combines transformers embeddings with topic information and offense features. The system
classifies social media text emotions leveraging its context representations. Our results show
that, for this kind of task, our model outperforms baselines and state-of-the-art text
classification methods. As for the leader-board, our classification model achieved a macro …
Abstract
This paper describes the system submitted by WSSC Team to the EmoEvalEs@ IberLEF 2021 emotions detection competition. We propose a novel model for Emotion Detection that combines transformers embeddings with topic information and offense features. The system classifies social media text emotions leveraging its context representations. Our results show that, for this kind of task, our model outperforms baselines and state-of-the-art text classification methods. As for the leader-board, our classification model achieved a macro weighted averaged F1 score of 0.661427, and a overall accuracy of 0.675725, reaching the 9th and 10th place respectively.
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