short-paper

EmotionSensing: Predicting Mobile User Emotion

Authors:

Mahnaz Roshanaei,

Shivakant MishraAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 325 - 330

https://doi.org/10.1145/3110025.3110127

Published: 31 July 2017 Publication History

Abstract

User emotions are important contextual features in building context-aware pervasive applications. In this paper, we explore the question of whether it is possible to predict user emotions from their smartphone activities. To get the ground truth data, we have built an Android app that collects user emotions along with a number of features including their current location, activity they are engaged in, and smartphones apps they are currently running. We deployed this app for over a period of three months and collected a large amount of useful user data. We describe the details of this data in terms of statistics and user behaviors, provide a detailed analysis in terms of correlations between user emotions and other features, and finally build classifiers to predict user emotions. Performance of these classifiers is quite promising with high accuracy. We describe the details of these classifiers along with the results.

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Cited By

Dorneles SFrancisco RBarbosa DBarbosa J(2022)Context Awareness in Recognition of Affective States: A Systematic Mapping of the LiteratureInternational Journal of Human–Computer Interaction10.1080/10447318.2022.206254939:8(1563-1581)Online publication date: 25-Apr-2022
https://doi.org/10.1080/10447318.2022.2062549
Kołakowska ASzwoch WSzwoch M(2020)A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone SensorsSensors10.3390/s2021636720:21(6367)Online publication date: 8-Nov-2020
https://doi.org/10.3390/s20216367
Dhahri CIkeda KHoashi KWolman AZhong L(2019)Forecasting Mood Using Smartphone and SNS DataProceedings of the 20th International Workshop on Mobile Computing Systems and Applications10.1145/3301293.3309561(175-175)Online publication date: 22-Feb-2019
https://dl.acm.org/doi/10.1145/3301293.3309561
Show More Cited By

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cover image ACM Conferences

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

Copyright © 2017 ACM.

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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Publication History

Published: 31 July 2017

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ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Dorneles SFrancisco RBarbosa DBarbosa J(2022)Context Awareness in Recognition of Affective States: A Systematic Mapping of the LiteratureInternational Journal of Human–Computer Interaction10.1080/10447318.2022.206254939:8(1563-1581)Online publication date: 25-Apr-2022
https://doi.org/10.1080/10447318.2022.2062549
Kołakowska ASzwoch WSzwoch M(2020)A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone SensorsSensors10.3390/s2021636720:21(6367)Online publication date: 8-Nov-2020
https://doi.org/10.3390/s20216367
Dhahri CIkeda KHoashi KWolman AZhong L(2019)Forecasting Mood Using Smartphone and SNS DataProceedings of the 20th International Workshop on Mobile Computing Systems and Applications10.1145/3301293.3309561(175-175)Online publication date: 22-Feb-2019
https://dl.acm.org/doi/10.1145/3301293.3309561
Soleimaninejadian PZhang MLiu YMa S(2018)Mood Detection and Prediction Based on User Daily Activities2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)10.1109/ACIIAsia.2018.8470365(1-6)Online publication date: May-2018
https://doi.org/10.1109/ACIIAsia.2018.8470365

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