TY - GEN
T1 - PROMO for Interpretable Personalized Social Emotion Mining
AU - Zhang, Jason (Jiasheng)
AU - Lee, Dongwon
N1 - Funding Information:
The authors would like to thank the anonymous referees and Noseong Park at GMU for their valuable comments. This work was supported in part by NSF awards #1422215, #1525601, #1742702, and Samsung GRO 2015 awards.
Funding Information:
Acknowledgement. The authors would like to thank the anonymous referees and Noseong Park at GMU for their valuable comments. This work was supported in part by NSF awards #1422215, #1525601, #1742702, and Samsung GRO 2015 awards.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Unearthing a set of users’ collective emotional reactions to news or posts in social media has many useful applications and business implications. For instance, when one reads a piece of news on Facebook with dominating “angry” reactions, or another with dominating “love” reactions, she may have a general sense on how social users react to the particular piece. However, such a collective view of emotion is unable to answer the subtle differences that may exist among users. To answer the question “which emotion who feels about what” better, therefore, we formulate the Personalized Social Emotion Mining (PSEM) problem. Solving the PSEM problem is non-trivial in that: (1) the emotional reaction data is in the form of ternary relationship among user-emotion-post, and (2) the results need to be interpretable. Addressing the two challenges, in this paper, we develop an expressive probabilistic generative model, PROMO, and demonstrate its validity through empirical studies.
AB - Unearthing a set of users’ collective emotional reactions to news or posts in social media has many useful applications and business implications. For instance, when one reads a piece of news on Facebook with dominating “angry” reactions, or another with dominating “love” reactions, she may have a general sense on how social users react to the particular piece. However, such a collective view of emotion is unable to answer the subtle differences that may exist among users. To answer the question “which emotion who feels about what” better, therefore, we formulate the Personalized Social Emotion Mining (PSEM) problem. Solving the PSEM problem is non-trivial in that: (1) the emotional reaction data is in the form of ternary relationship among user-emotion-post, and (2) the results need to be interpretable. Addressing the two challenges, in this paper, we develop an expressive probabilistic generative model, PROMO, and demonstrate its validity through empirical studies.
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U2 - 10.1007/978-3-030-67658-2_15
DO - 10.1007/978-3-030-67658-2_15
M3 - Conference contribution
AN - SCOPUS:85103294345
SN - 9783030676575
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 249
EP - 265
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings
A2 - Hutter, Frank
A2 - Kersting, Kristian
A2 - Lijffijt, Jefrey
A2 - Valera, Isabel
PB - Springer Science and Business Media Deutschland GmbH
T2 - European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
Y2 - 14 September 2020 through 18 September 2020
ER -