Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis

TL;DR

This paper introduces a multi-task neural network that leverages emotion analysis to enhance sentiment prediction, utilizing hierarchical attention and external knowledge sources, achieving improved performance on benchmark datasets.

Contribution

A novel two-layered multi-task attention neural network that jointly models sentiment and emotion analysis with external knowledge integration.

Findings

Improves sentiment analysis F-score by 3.2 points on SemEval 2016 dataset.

Boosts emotion analysis F-score by 5 points on Stance Sentiment Emotion Corpus.

Employs hierarchical attention and external knowledge for better representation.

Abstract

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.