  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.
