Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
This work addresses sentiment and emotion analysis for natural language processing applications, representing an incremental improvement over existing methods.
The paper tackles sentiment analysis by leveraging emotion analysis through a multi-task neural network, resulting in a 3.2 F-score improvement on SemEval 2016 Task 6 and a 5 F-score boost on Stance Sentiment Emotion Corpus.
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.