ROAR: Robust label ranking for social emotion mining

Jason Jiasheng Zhang, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Understanding and predicting latent emotions of users toward online contents, known as social emotion mining, has became increasingly important to both social platforms and businesses alike. Despite recent developments, however, very little attention has been made to the issues of nuance, subjectivity, and bias of social emotions. In this paper, we fill this gap by formulating social emotion mining as a robust label ranking problem, and propose: (1) a robust measure, named as G-mean-rank (GMR), which sets a formal criterion consistent with practical intuition; and (2) a simple yet effective label ranking model, named as ROAR, that is more robust toward unbalanced datasets (which are common). Through comprehensive empirical validation using 4 real datasets and 16 benchmark semi-synthetic label ranking datasets, and a case study, we demonstrate the superiorities of our proposals over 2 popular label ranking measures and 6 competing label ranking algorithms. The datasets and implementations used in the empirical validation are available for access 1

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages4422-4429
Number of pages8
ISBN (Electronic)9781577358008
StatePublished - Jan 1 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

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Labels
Industry

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Jiasheng Zhang, J., & Lee, D. (2018). ROAR: Robust label ranking for social emotion mining. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 4422-4429). (32nd AAAI Conference on Artificial Intelligence, AAAI 2018). AAAI press.
Jiasheng Zhang, Jason ; Lee, Dongwon. / ROAR : Robust label ranking for social emotion mining. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. pp. 4422-4429 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).
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abstract = "Understanding and predicting latent emotions of users toward online contents, known as social emotion mining, has became increasingly important to both social platforms and businesses alike. Despite recent developments, however, very little attention has been made to the issues of nuance, subjectivity, and bias of social emotions. In this paper, we fill this gap by formulating social emotion mining as a robust label ranking problem, and propose: (1) a robust measure, named as G-mean-rank (GMR), which sets a formal criterion consistent with practical intuition; and (2) a simple yet effective label ranking model, named as ROAR, that is more robust toward unbalanced datasets (which are common). Through comprehensive empirical validation using 4 real datasets and 16 benchmark semi-synthetic label ranking datasets, and a case study, we demonstrate the superiorities of our proposals over 2 popular label ranking measures and 6 competing label ranking algorithms. The datasets and implementations used in the empirical validation are available for access 1",
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Jiasheng Zhang, J & Lee, D 2018, ROAR: Robust label ranking for social emotion mining. in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, AAAI press, pp. 4422-4429, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, New Orleans, United States, 2/2/18.

ROAR : Robust label ranking for social emotion mining. / Jiasheng Zhang, Jason; Lee, Dongwon.

32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. p. 4422-4429 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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M3 - Conference contribution

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Jiasheng Zhang J, Lee D. ROAR: Robust label ranking for social emotion mining. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press. 2018. p. 4422-4429. (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).