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User interest and social influence based emotion prediction for individuals

Authors:

Yun Yang,

Peng Cui,

Wenwu Zhu,

Shiqiang YangAuthors Info & Claims

MM '13: Proceedings of the 21st ACM international conference on Multimedia

Pages 785 - 788

https://doi.org/10.1145/2502081.2502204

Published: 21 October 2013 Publication History

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Abstract

Emotions are playing significant roles in daily life, making emotion prediction important. To date, most of state-of-the-art methods make emotion prediction for the masses which are invalid for individuals. In this paper, we propose a novel emotion prediction method for individuals based on user interest and social influence. To balance user interest and social influence, we further propose a simple yet efficient weight learning method in which the weights are obtained from users' behaviors. We perform experiments in real social media network, with 4,257 users and 2,152,037 microblogs. The experimental results demonstrate that our method outperforms traditional methods with significant performance gains.

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Xue JWang JLiu XZhang QWu X(2025)Affective Video Content Analysis: Decade Review and New PerspectivesBig Data Mining and Analytics10.26599/BDMA.2024.90200488:1(118-144)Online publication date: Feb-2025
https://doi.org/10.26599/BDMA.2024.9020048
Wu DYang DZhou YMa CCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Bridging Visual Affective Gap: Borrowing Textual Knowledge by Learning from Noisy Image-Text PairsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680875(602-611)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680875
Ding KFan CDing YWang QWen ZLi JXu R(2024)LCSEP: A Large-Scale Chinese Dataset for Social Emotion Prediction to Online Trending TopicsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333429611:3(3362-3375)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3334296
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Index Terms

User interest and social influence based emotion prediction for individuals
1. Applied computing
  1. Law, social and behavioral sciences

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Information & Contributors

Information

Published In

MM '13: Proceedings of the 21st ACM international conference on Multimedia

October 2013

1166 pages

ISBN:9781450324045

DOI:10.1145/2502081

General Chairs:
Alejandro (Alex) Jaimes
Yahoo!, Spain
,
Nicu Sebe
University of Trento, Italy
,
Nozha Boujemaa
INRIA, France
,
Program Chairs:
Daniel Gatica-Perez
IDIAP & EPFL, Switzerland
,
David A. Shamma
Yahoo!, USA
,
Marcel Worring
University of Amsterdam, The Netherlands
,
Roger Zimmermann
National University of Singapore, Singapore

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2013

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MM '13

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SIGMM

MM '13: ACM Multimedia Conference

October 21 - 25, 2013

Barcelona, Spain

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MM '13 Paper Acceptance Rate 47 of 235 submissions, 20%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Xue JWang JLiu XZhang QWu X(2025)Affective Video Content Analysis: Decade Review and New PerspectivesBig Data Mining and Analytics10.26599/BDMA.2024.90200488:1(118-144)Online publication date: Feb-2025
https://doi.org/10.26599/BDMA.2024.9020048
Wu DYang DZhou YMa CCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Bridging Visual Affective Gap: Borrowing Textual Knowledge by Learning from Noisy Image-Text PairsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680875(602-611)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680875
Ding KFan CDing YWang QWen ZLi JXu R(2024)LCSEP: A Large-Scale Chinese Dataset for Social Emotion Prediction to Online Trending TopicsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333429611:3(3362-3375)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3334296
Korbel JRiar MPröhl TZarnekow R(2024)What drives user interest and purchase of virtual 3D assets? An empirical investigation of 3D model attributes and pricing dynamicsElectronic Commerce Research and Applications10.1016/j.elerap.2024.10145268(101452)Online publication date: Nov-2024
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Hai-Jew S(2022)Self-Presenting Virtually for Remote Social InfluencePractical Peer-to-Peer Teaching and Learning on the Social Web10.4018/978-1-7998-6496-7.ch013(407-461)Online publication date: 2022
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Zhang JLee D(2021)PROMO for Interpretable Personalized Social Emotion MiningMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-67658-2_15(249-265)Online publication date: 25-Feb-2021
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Zhao SZhao XDing GKeutzer KBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)EmotionGANProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240591(1319-1327)Online publication date: 15-Oct-2018
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Zhao PJia JAn YLiang JXie LLuo JChampin PGandon FMédini LLalmas MIpeirotis P(2018)Analyzing and Predicting Emoji Usages in Social MediaCompanion Proceedings of the The Web Conference 201810.1145/3184558.3186344(327-334)Online publication date: 23-Apr-2018
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