A social-event based approach to sentiment analysis of identities and behaviors in text

We describe a new methodology to infer sentiments held towards identities and behaviors from social events that we extract from a large corpus of newspaper text. Our approach draws on Affect Control Theory, a mathematical model of how sentiment is encoded in social events and culturally-shared views towards identities and behaviors. While most sentiment analysis approaches evaluate concepts on a single, evaluative dimension, our work extracts a threedimensional sentiment “profile” for each concept. We can also infer when multiple sentiment profiles for a concept are likely to exist. We provide a case study of a large newspaper corpus on the Arab Spring, which helps to validate our approach.