  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.
