Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection [article]

Prabod Rathnayaka, Supun Abeysinghe, Chamod Samarajeewa, Isura Manchanayake, Malaka Walpola
<span title="2018-09-05">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we present the system we have used for the Implicit WASSA 2018 Implicit Emotion Shared Task. The task is to predict the emotion of a tweet of which the explicit mentions of emotion terms have been removed. The idea is to come up with a model which has the ability to implicitly identify the emotion expressed given the context words. We have used a Gated Recurrent Neural Network (GRU) and a Capsule Network based model for the task. Pre-trained word embeddings have been utilized to
more &raquo; ... ncorporate contextual knowledge about words into the model. GRU layer learns latent representations using the input word embeddings. Subsequent Capsule Network layer learns high-level features from that hidden representation. The proposed model managed to achieve a macro-F1 score of 0.692.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1809.01452v1</a> <a target="_blank" rel="external noopener" href="">fatcat:z223bh5w6banbmlo7ikavz2hh4</a> </span>
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