In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses Distributional Thesaurus as a source of external knowledge to improve the sentiment and emotion prediction. The proposed system has two levels of attention to hierarchically build a meaningful representation. We evaluate our system on the benchmark dataset of SemEval 2016 Task 6 and also compare it with the state-of-the-art systems on Stance Sentiment Emotion Corpus. Experimental results show that the proposed system improves the performance of sentiment analysis by 3.2 F-score points on SemEval 2016 Task 6 dataset. Our network also boosts the performance of emotion analysis by 5 F-score points on Stance Sentiment Emotion Corpus.
@article{arxiv.1911.12569,
title = {Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis},
author = {Abhishek Kumar and Asif Ekbal and Daisuke Kawahra and Sadao Kurohashi},
journal= {arXiv preprint arXiv:1911.12569},
year = {2019}
}
Comments
Accepted in the Proceedings of The 2019 IEEE International Joint Conference on Neural Networks (IJCNN 2019)