A social-event based approach to sentiment analysis of identities and behaviors in text |
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Authors: | Kenneth Joseph Wei Wei Matthew Benigni Kathleen M Carley |
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Institution: | 1. Societal Computing Program, Carnegie Mellon University, Pittsburgh, Pennsylvania, USAkjoseph@cs.cmu.edu;3. Societal Computing Program, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA |
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Abstract: | We describe a new methodology to infer sentiments held toward 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 toward identities and behaviors. While most sentiment analysis approaches evaluate concepts on a single, evaluative dimension, our work extracts a three-dimensional 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. |
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Keywords: | Affect control theory Arab Spring Bayesian inference machine learning natural language processing sentiment analysis |
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