Discerning Emotions in Texts

Watson and Tellegen’s Circumplex Theory of Affect appears to be usable as a guide for development of an NLP algorithm for automated identification of emotion in English texts, and the non-expert informants provided sufficient information for future creation of a gold standard of clues per category.

Abstract

We present an empirically verified model of discernable emotions, Watson and Tellegen’s Circumplex Theory of Affect from social and personality psychology, and suggest its usefulness in NLP as a potential model for an automation of an eight-fold categorization of emotions in written English texts. We developed a data collection tool based on the model, collected 287 responses from 110 non-expert informants based on 50 emotional excerpts (min=12, max=348, average=86 words), and analyzed the inter-coder