The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
다가오는 기분 예측은 심리학의 양극성 우울증 장애, 삶의 질, 건강 관련 삶의 질 연구에 대한 권장사항 등 다양한 주제에서 중요한 역할을 합니다. 본 연구에서 기분은 사용자의 일반적인 감정 상태로 정의된다. 하루 동안 더욱 구체적이고 변화하는 감정과는 대조적으로, 기분은 긍정적이거나 부정적인 원자가를 갖는 것으로 설명됩니다[1]. 예측하는 자율 시스템을 제안합니다. 곧 출시 별도의 장치나 센서를 사용하지 않고 사이버, 소셜, 물리적 공간에서의 온라인 활동을 기반으로 사용자 기분을 파악합니다. 최근 많은 연구자들이 사용자 기분을 감지하기 위해 온라인 소셜 네트워크(OSN)에 의존해 왔습니다. 그러나 기존의 작품들은 모두 현재의 기분을 추론하는 데 초점을 맞추고 있으며, 앞으로의 기분을 예측하는 데 초점을 맞춘 작품은 거의 없다. 이러한 이유로 우리는 다가오는 기분을 예측한다는 새로운 목표를 정의합니다. 우리는 먼저 383명의 피험자로부터 두 달 동안 Ground Truth 데이터를 수집했습니다. 그리고 추출된 특징과 사용자의 기분 사이의 상관관계를 연구했습니다. 마지막으로 우리는 이러한 기능을 사용하여 일반화 및 개인화라는 두 가지 예측 시스템을 훈련했습니다. 결과는 OSN에서 내일의 기분과 오늘의 활동 사이에 통계적으로 유의미한 상관관계가 있음을 시사하며, 이는 상관된 사용자에 대해 평균 70%의 정확도와 75%의 회상률을 갖춘 적절한 예측 시스템을 개발하는 데 사용될 수 있습니다. 이 성능은 79일 이상의 이력 데이터를 보유한 활성 사용자의 경우 평균 정확도 80%, 재현율 30%로 향상되었습니다. 또한, 비활성 사용자의 경우 일반화된 시스템을 참조하는 것이 시스템 초기 단계에서는 데이터 부족을 보완하는 솔루션이 될 수 있지만, 각 사용자에 대한 충분한 데이터가 있을 경우 개인화된 시스템을 사용한다는 것을 보여주었다. 다가오는 기분을 개별적으로 예측합니다.
Chaima DHAHRI
KDDI Research
Kazunori MATSUMOTO
KDDI Research
Keiichiro HOASHI
KDDI Research
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Chaima DHAHRI, Kazunori MATSUMOTO, Keiichiro HOASHI, "Upcoming Mood Prediction Using Public Online Social Networks Data: Analysis over Cyber-Social-Physical Dimension" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1625-1634, September 2019, doi: 10.1587/transinf.2018OFP0006.
Abstract: Upcoming mood prediction plays an important role in different topics such as bipolar depression disorder in psychology and quality-of-life and recommendations on health-related quality of life research. The mood in this study is defined as the general emotional state of a user. In contrast to emotions which is more specific and varying within a day, the mood is described as having either a positive or negative valence[1]. We propose an autonomous system that predicts the upcoming user mood based on their online activities over cyber, social and physical spaces without using extra-devices and sensors. Recently, many researchers have relied on online social networks (OSNs) to detect user mood. However, all the existing works focused on inferring the current mood and only few works have focused on predicting the upcoming mood. For this reason, we define a new goal of predicting the upcoming mood. We, first, collected ground truth data during two months from 383 subjects. Then, we studied the correlation between extracted features and user's mood. Finally, we used these features to train two predictive systems: generalized and personalized. The results suggest a statistically significant correlation between tomorrow's mood and today's activities on OSNs, which can be used to develop a decent predictive system with an average accuracy of 70% and a recall of 75% for the correlated users. This performance was increased to an average accuracy of 79% and a recall of 80% for active users who have more than 30 days of history data. Moreover, we showed that, for non-active users, referring to a generalized system can be a solution to compensate the lack of data at the early stage of the system, but when enough data for each user is available, a personalized system is used to individually predict the upcoming mood.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018OFP0006/_p
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@ARTICLE{e102-d_9_1625,
author={Chaima DHAHRI, Kazunori MATSUMOTO, Keiichiro HOASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Upcoming Mood Prediction Using Public Online Social Networks Data: Analysis over Cyber-Social-Physical Dimension},
year={2019},
volume={E102-D},
number={9},
pages={1625-1634},
abstract={Upcoming mood prediction plays an important role in different topics such as bipolar depression disorder in psychology and quality-of-life and recommendations on health-related quality of life research. The mood in this study is defined as the general emotional state of a user. In contrast to emotions which is more specific and varying within a day, the mood is described as having either a positive or negative valence[1]. We propose an autonomous system that predicts the upcoming user mood based on their online activities over cyber, social and physical spaces without using extra-devices and sensors. Recently, many researchers have relied on online social networks (OSNs) to detect user mood. However, all the existing works focused on inferring the current mood and only few works have focused on predicting the upcoming mood. For this reason, we define a new goal of predicting the upcoming mood. We, first, collected ground truth data during two months from 383 subjects. Then, we studied the correlation between extracted features and user's mood. Finally, we used these features to train two predictive systems: generalized and personalized. The results suggest a statistically significant correlation between tomorrow's mood and today's activities on OSNs, which can be used to develop a decent predictive system with an average accuracy of 70% and a recall of 75% for the correlated users. This performance was increased to an average accuracy of 79% and a recall of 80% for active users who have more than 30 days of history data. Moreover, we showed that, for non-active users, referring to a generalized system can be a solution to compensate the lack of data at the early stage of the system, but when enough data for each user is available, a personalized system is used to individually predict the upcoming mood.},
keywords={},
doi={10.1587/transinf.2018OFP0006},
ISSN={1745-1361},
month={September},}
부
TY - JOUR
TI - Upcoming Mood Prediction Using Public Online Social Networks Data: Analysis over Cyber-Social-Physical Dimension
T2 - IEICE TRANSACTIONS on Information
SP - 1625
EP - 1634
AU - Chaima DHAHRI
AU - Kazunori MATSUMOTO
AU - Keiichiro HOASHI
PY - 2019
DO - 10.1587/transinf.2018OFP0006
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - Upcoming mood prediction plays an important role in different topics such as bipolar depression disorder in psychology and quality-of-life and recommendations on health-related quality of life research. The mood in this study is defined as the general emotional state of a user. In contrast to emotions which is more specific and varying within a day, the mood is described as having either a positive or negative valence[1]. We propose an autonomous system that predicts the upcoming user mood based on their online activities over cyber, social and physical spaces without using extra-devices and sensors. Recently, many researchers have relied on online social networks (OSNs) to detect user mood. However, all the existing works focused on inferring the current mood and only few works have focused on predicting the upcoming mood. For this reason, we define a new goal of predicting the upcoming mood. We, first, collected ground truth data during two months from 383 subjects. Then, we studied the correlation between extracted features and user's mood. Finally, we used these features to train two predictive systems: generalized and personalized. The results suggest a statistically significant correlation between tomorrow's mood and today's activities on OSNs, which can be used to develop a decent predictive system with an average accuracy of 70% and a recall of 75% for the correlated users. This performance was increased to an average accuracy of 79% and a recall of 80% for active users who have more than 30 days of history data. Moreover, we showed that, for non-active users, referring to a generalized system can be a solution to compensate the lack of data at the early stage of the system, but when enough data for each user is available, a personalized system is used to individually predict the upcoming mood.
ER -