PROMO for Interpretable Personalized Social Emotion Mining

Jason (Jiasheng) Zhang, Dongwon Lee

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

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings
EditorsFrank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030676575
StatePublished - 2021
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Duration: Sep 14 2020Sep 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12457 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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