基于大五理论的推特用户性格分类的k近邻算法

Agatha Silvani Sekarningtyas, M. A. Ayu, T. Mantoro
{"title":"基于大五理论的推特用户性格分类的k近邻算法","authors":"Agatha Silvani Sekarningtyas, M. A. Ayu, T. Mantoro","doi":"10.1109/ICCED53389.2021.9664857","DOIUrl":null,"url":null,"abstract":"Social media is an application or website-based system that enables users to create and share content or participate in social networking that allows its user to share their thoughts, opinions, or feelings that represent their personality. At present several studies to classify an individual's personality through social media have been developed, especially on social media Twitter. However, most of the analysis on Twitter only uses text based data such as posted tweets. This research presents a study on analyzing the users’ twitter data to classify their types of personality based on Big Five Theory by using their social statistic data. The data were acquired using Twitter API which was taken from Indonesian users with the total of 225 data. This study shows that using K-Nearest Neighbor (K-NN) Algorithm for classification of these data were not resulting in high accuracy. However, this study has shown that amount and balance distribution of training data critically contribute to the performance of classification process.","PeriodicalId":6800,"journal":{"name":"2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED)","volume":"5 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Using K-Nearest Neighbor Algorithm for Personality Classification of Twitter’s Users Based on the Big Five Theory\",\"authors\":\"Agatha Silvani Sekarningtyas, M. A. Ayu, T. Mantoro\",\"doi\":\"10.1109/ICCED53389.2021.9664857\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social media is an application or website-based system that enables users to create and share content or participate in social networking that allows its user to share their thoughts, opinions, or feelings that represent their personality. At present several studies to classify an individual's personality through social media have been developed, especially on social media Twitter. However, most of the analysis on Twitter only uses text based data such as posted tweets. This research presents a study on analyzing the users’ twitter data to classify their types of personality based on Big Five Theory by using their social statistic data. The data were acquired using Twitter API which was taken from Indonesian users with the total of 225 data. This study shows that using K-Nearest Neighbor (K-NN) Algorithm for classification of these data were not resulting in high accuracy. However, this study has shown that amount and balance distribution of training data critically contribute to the performance of classification process.\",\"PeriodicalId\":6800,\"journal\":{\"name\":\"2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED)\",\"volume\":\"5 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCED53389.2021.9664857\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCED53389.2021.9664857","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

社交媒体是一种基于应用程序或网站的系统,它使用户能够创建和分享内容或参与社交网络,允许用户分享他们的想法、观点或代表他们个性的感受。目前已经开展了几项通过社交媒体,特别是社交媒体Twitter对个人性格进行分类的研究。然而,Twitter上的大多数分析只使用基于文本的数据,比如发布的tweet。本研究利用用户的社会统计数据,对用户的推特数据进行基于大五人格理论的分类研究。这些数据是使用Twitter API获得的,该API来自印度尼西亚用户,共有225个数据。本研究表明,使用k -最近邻(K-NN)算法对这些数据进行分类并没有得到很高的准确率。然而,本研究表明,训练数据的数量和平衡分布对分类过程的性能有重要影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Using K-Nearest Neighbor Algorithm for Personality Classification of Twitter’s Users Based on the Big Five Theory
Social media is an application or website-based system that enables users to create and share content or participate in social networking that allows its user to share their thoughts, opinions, or feelings that represent their personality. At present several studies to classify an individual's personality through social media have been developed, especially on social media Twitter. However, most of the analysis on Twitter only uses text based data such as posted tweets. This research presents a study on analyzing the users’ twitter data to classify their types of personality based on Big Five Theory by using their social statistic data. The data were acquired using Twitter API which was taken from Indonesian users with the total of 225 data. This study shows that using K-Nearest Neighbor (K-NN) Algorithm for classification of these data were not resulting in high accuracy. However, this study has shown that amount and balance distribution of training data critically contribute to the performance of classification process.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Risk Analysis and Mitigation in Supply Chain Fashion Company Image Steganography Using Steg with AES and LSB National Resilience Index Model and Public Policy Simulation Analysis Of Road Geometric Standards In Hilling Areas Using Bim Mini UAV Orientation Control based on Face Tracking Algorithm
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1