Emotion Detection with Neural Personal Discrimination
This addresses emotion prediction for social media users by incorporating personal attributes, though it is incremental as it builds on existing methods by adding attribute-based correlation.
The paper tackles emotion detection in social media posts by exploring dependence among posts through authors' personal attributes, proposing a Neural Personal Discrimination approach that uses adversarial discriminators and attention mechanisms to determine attributes and connect similar posts, achieving significant gains over state-of-the-art models.
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.