Emotion Detection with Neural Personal Discrimination

TL;DR

This paper introduces a neural approach that leverages inferred personal attributes from social media posts to improve emotion detection by modeling the dependence among posts with similar backgrounds.

Contribution

It proposes Neural Personal Discrimination (NPD), a novel method that infers personal attributes and connects posts with similar attributes to enhance emotion prediction accuracy.

Findings

NPD outperforms state-of-the-art models in emotion detection.

Personal attributes inferred by NPD improve model performance.

Attention mechanisms effectively identify attributes-aware words.

Abstract

There have been a recent line of works to automatically predict the emotions of posts in social media. Existing approaches consider the posts individually and predict their emotions independently. Different from previous researches, we explore the dependence among relevant posts via the authors' backgrounds, since the authors with similar backgrounds, e.g., gender, location, tend to express similar emotions. However, such personal attributes are not easy to obtain in most social media websites, and it is hard to capture attributes-aware words to connect similar people. Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions. In particular, we employ adversarial discriminators to determine the personal attributes, with attention mechanisms to aggregate attributes-aware words. In this way, social correlationship among different posts can be better addressed. Experimental results show the usefulness of personal attributes, and the effectiveness of our proposed NPD approach in capturing such personal attributes with significant gains over the state-of-the-art models.