Automatic event-level textual emotion sensing using mutual action histogram between entities

Cheng-Yu Lu (1) , Shian-Hua Lin (1) , Jen-Chang Liu (1) , Samuel Cruz-Lara (2) , Jen-Shin Hong (3)

1 Department of Computer Science and Information Engineering
2 TALARIS - Natural Language Processing: representation, inference and semantics
3 National ChiNan University

Automatic emotion sensing in textual data is crucial for the development of intelligent interfaces in many interactive computer applications. This paper describes a high-precision, knowledgebase-independent approach for automatic emotion sensing for the subjects of events embedded within sentences. The proposed approach is based on the probability distribution of common mutual actions between the subject and the object of an event. We have incorporated web-based text mining and semantic role labeling techniques, together with a number of reference entity pairs and hand-crafted emotion generation rules to realize an event emotion detection system. The evaluation outcome reveals a satisfactory result with about 85% accuracy for detecting the positive, negative and neutral emotions.

Mots clés: Emotion sensing; Web text mining; Semantic role labeling; Affect recognition

Domaines: Informatique et langage [cs.CL]

Cheng-Yu Lu, Shian-Hua Lin, Jen-Chang Liu, Samuel Cruz-Lara, Jen-Shin Hong. Automatic event-level textual emotion sensing using mutual action histogram between entities. Expert Systems with Applications, 2009, 37 (2), pp.1643-1653. ⟨10.1016/j.eswa.2009.06.099⟩. ⟨inria-00426567⟩