A model of textual affect sensing using real-world knowledge

  • Authors:
  • Hugo Liu;Henry Lieberman;Ted Selker

  • Affiliations:
  • MIT Media Laboratory, Cambridge, MA;MIT Media Laboratory, Cambridge, MA;MIT Media Laboratory, Cambridge, MA

  • Venue:
  • Proceedings of the 8th international conference on Intelligent user interfaces
  • Year:
  • 2003

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Abstract

This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and hand-crafted models. This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories. This commonsense approach has new robustness implications.Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text. Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces.