OCC model-based emotion extraction from online reviews

Luwen Huangfu; Wenji Mao; Daniel Zeng; Lei Wang

doi:10.1109/ISI.2013.6578799

OCC model-based emotion extraction from online reviews

Luwen Huangfu, Wenji Mao, Daniel Zeng, Lei Wang

Management Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics
Subtitle of host publication	Big Data, Emergent Threats, and Decision-Making in Security Informatics
Pages	116-121
Number of pages	6
DOIs	https://doi.org/10.1109/ISI.2013.6578799
State	Published - Sep 9 2013
Event	11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013 - Seattle, WA, United States Duration: Jun 4 2013 → Jun 7 2013

Publication series

Name	IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics

Other

Other	11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013
Country	United States
City	Seattle, WA
Period	6/4/13 → 6/7/13

Keywords

OCC emotion model
emotional dimensions
opinion mining

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

Access to Document

10.1109/ISI.2013.6578799

Fingerprint Dive into the research topics of 'OCC model-based emotion extraction from online reviews'. Together they form a unique fingerprint.

Cite this

Huangfu, L., Mao, W., Zeng, D., & Wang, L. (2013). OCC model-based emotion extraction from online reviews. In IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics (pp. 116-121). [6578799] (IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics). https://doi.org/10.1109/ISI.2013.6578799

OCC model-based emotion extraction from online reviews. / Huangfu, Luwen; Mao, Wenji; Zeng, Daniel; Wang, Lei.

IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics. 2013. p. 116-121 6578799 (IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Huangfu, L, Mao, W, Zeng, D & Wang, L 2013, OCC model-based emotion extraction from online reviews. in IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics., 6578799, IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics, pp. 116-121, 11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013, Seattle, WA, United States, 6/4/13. https://doi.org/10.1109/ISI.2013.6578799

Huangfu L, Mao W, Zeng D, Wang L. OCC model-based emotion extraction from online reviews. In IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics. 2013. p. 116-121. 6578799. (IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics). https://doi.org/10.1109/ISI.2013.6578799

Huangfu, Luwen ; Mao, Wenji ; Zeng, Daniel ; Wang, Lei. / OCC model-based emotion extraction from online reviews. IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics. 2013. pp. 116-121 (IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics).

@inproceedings{230b6a2aa9944fa3bd4166944d993a23,

title = "OCC model-based emotion extraction from online reviews",

abstract = "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.",

keywords = "OCC emotion model, emotional dimensions, opinion mining",

author = "Luwen Huangfu and Wenji Mao and Daniel Zeng and Lei Wang",

year = "2013",

month = sep,

day = "9",

doi = "10.1109/ISI.2013.6578799",

language = "English (US)",

isbn = "9781467362115",

series = "IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics",

pages = "116--121",

booktitle = "IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics",

note = "11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013 ; Conference date: 04-06-2013 Through 07-06-2013",

}

TY - GEN

T1 - OCC model-based emotion extraction from online reviews

AU - Huangfu, Luwen

AU - Mao, Wenji

AU - Zeng, Daniel

AU - Wang, Lei

PY - 2013/9/9

Y1 - 2013/9/9

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 -