基于潜在狄利克雷分配算法的Twitter社交媒体转换话题趋势分析

Q3 Engineering
Musliadi K H, Hazriani Zainuddin, Yuyun Wabula
{"title":"基于潜在狄利克雷分配算法的Twitter社交媒体转换话题趋势分析","authors":"Musliadi K H, Hazriani Zainuddin, Yuyun Wabula","doi":"10.37385/jaets.v4i1.1143","DOIUrl":null,"url":null,"abstract":"In Indonesia, Twitter is one of the most widely used social media platforms. Because of the diverse and frequently shifting message patterns on this social media, it is extremely challenging and time-consuming to manually identify topics from a collection of messages. Topic modeling is one method for obtaining information from social media. The model and visualization of the results of modeling topics that are discussed on social media by the Makassar community are the goals of this study. The Latent Dirichlet Allocation (LDA) algorithm is used to model and display the results of this study. The modeling results indicate that the eighth topic is the most frequently used word in a conversation. In the meantime, the 7th and 6th topics emerged as the conversation's core based on the spread of the words with the highest term frequency. The study's findings led the researchers to the conclusion that in the Makassar community's social media discussions, capitalization and visualization using the LDA method produced the words with the highest trend and the topic with the highest term frequency.","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm\",\"authors\":\"Musliadi K H, Hazriani Zainuddin, Yuyun Wabula\",\"doi\":\"10.37385/jaets.v4i1.1143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In Indonesia, Twitter is one of the most widely used social media platforms. Because of the diverse and frequently shifting message patterns on this social media, it is extremely challenging and time-consuming to manually identify topics from a collection of messages. Topic modeling is one method for obtaining information from social media. The model and visualization of the results of modeling topics that are discussed on social media by the Makassar community are the goals of this study. The Latent Dirichlet Allocation (LDA) algorithm is used to model and display the results of this study. The modeling results indicate that the eighth topic is the most frequently used word in a conversation. In the meantime, the 7th and 6th topics emerged as the conversation's core based on the spread of the words with the highest term frequency. The study's findings led the researchers to the conclusion that in the Makassar community's social media discussions, capitalization and visualization using the LDA method produced the words with the highest trend and the topic with the highest term frequency.\",\"PeriodicalId\":34350,\"journal\":{\"name\":\"Journal of Applied Engineering and Technological Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Engineering and Technological Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37385/jaets.v4i1.1143\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Engineering and Technological Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37385/jaets.v4i1.1143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 2

摘要

在印度尼西亚,推特是使用最广泛的社交媒体平台之一。由于社交媒体上的消息模式多样且经常变化,从消息集合中手动识别主题极具挑战性且耗时。主题建模是从社交媒体获取信息的一种方法。Makassar社区在社交媒体上讨论的建模主题的模型和结果可视化是本研究的目标。使用潜在狄利克雷分配(LDA)算法对本研究的结果进行建模和显示。建模结果表明,第八个话题是会话中使用频率最高的词。与此同时,第7和第6个话题成为对话的核心,这是基于术语频率最高的单词的传播。研究结果使研究人员得出结论,在望加锡社区的社交媒体讨论中,使用LDA方法的大写和可视化产生了趋势最高的单词和术语频率最高的话题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm
In Indonesia, Twitter is one of the most widely used social media platforms. Because of the diverse and frequently shifting message patterns on this social media, it is extremely challenging and time-consuming to manually identify topics from a collection of messages. Topic modeling is one method for obtaining information from social media. The model and visualization of the results of modeling topics that are discussed on social media by the Makassar community are the goals of this study. The Latent Dirichlet Allocation (LDA) algorithm is used to model and display the results of this study. The modeling results indicate that the eighth topic is the most frequently used word in a conversation. In the meantime, the 7th and 6th topics emerged as the conversation's core based on the spread of the words with the highest term frequency. The study's findings led the researchers to the conclusion that in the Makassar community's social media discussions, capitalization and visualization using the LDA method produced the words with the highest trend and the topic with the highest term frequency.
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
CiteScore
1.50
自引率
0.00%
发文量
0
审稿时长
4 weeks
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信