PROMO for Interpretable Personalized Social Emotion Mining
PROMO for Interpretable Personalized Social Emotion Mining
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DOI：
10.1007/978-3-030-67658-2_15
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Jason Zhang;Dongwon Lee
中科院分区：
文献类型：
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作者：
Jason Zhang;Dongwon Lee
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 thePersonalized 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 beinterpretable. Addressing the two challenges, in this paper, we develop an expressive probabilistic generative model, PROMO, and demonstrate its validity through empirical studies.
