TY - GEN
T1 - OCC model-based emotion extraction from online reviews
AU - Huangfu, Luwen
AU - Mao, Wenji
AU - Zeng, Daniel
AU - Wang, Lei
PY - 2013
Y1 - 2013
N2 - Extracting emotions from online reviews is crucial to many security-related applications as well as applications in other domains. Traditional approaches to emotion extraction have mainly focused on mining the polarities of opinions or using annotated data to extract emotion types. Emotion theories, which identify the underlying cognitive structure and emotional dimensions that are key to generate emotions, have almost been totally ignored in previous work. To facilitate the automatic extraction of emotions from textual data, in this paper, we propose an emotion model based approach to emotion extraction from online reviews. Informed by the widely used OCC emotion model, we employ a statistical method to extract emotion words with their dimension values from texts, and implement OCC model to obtain emotions based on the emotion-dimension dictionary. We conduct an empirical study using security-related news reviews. The experimental results demonstrate the effectiveness of our proposed approach.
AB - Extracting emotions from online reviews is crucial to many security-related applications as well as applications in other domains. Traditional approaches to emotion extraction have mainly focused on mining the polarities of opinions or using annotated data to extract emotion types. Emotion theories, which identify the underlying cognitive structure and emotional dimensions that are key to generate emotions, have almost been totally ignored in previous work. To facilitate the automatic extraction of emotions from textual data, in this paper, we propose an emotion model based approach to emotion extraction from online reviews. Informed by the widely used OCC emotion model, we employ a statistical method to extract emotion words with their dimension values from texts, and implement OCC model to obtain emotions based on the emotion-dimension dictionary. We conduct an empirical study using security-related news reviews. The experimental results demonstrate the effectiveness of our proposed approach.
KW - OCC emotion model
KW - emotional dimensions
KW - opinion mining
UR - http://www.scopus.com/inward/record.url?scp=84883381149&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883381149&partnerID=8YFLogxK
U2 - 10.1109/ISI.2013.6578799
DO - 10.1109/ISI.2013.6578799
M3 - Conference contribution
AN - SCOPUS:84883381149
SN - 9781467362115
T3 - IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics
SP - 116
EP - 121
BT - IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics
T2 - 11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013
Y2 - 4 June 2013 through 7 June 2013
ER -