如何提高词云的语义理解

Lu-Biao Yang, Jie Li, Wenhuan Lu, Yi Chen, Kang Zhang, Yan Li
{"title":"如何提高词云的语义理解","authors":"Lu-Biao Yang, Jie Li, Wenhuan Lu, Yi Chen, Kang Zhang, Yan Li","doi":"10.1145/3356422.3356449","DOIUrl":null,"url":null,"abstract":"Word cloud is a text visualization technique which is widely applied in helping improve semantic understanding about target materials. One of the most important features is the font size, which represents words frequencies of a document. As the result, in this paper, we explore how to set font sizes of words, and its influence on semantic understanding through people's performance with qualitative and controlled experiments. Adopting an machine learning algorithm LDA (Latent Dirichlet Allocation) topic model, we quantify semantics of the document and judge participants' accuracy performance. The experimental results show the influence of different font size on semantic understanding performance and provide insights for ways in promoting semantic understanding of word cloud.","PeriodicalId":197051,"journal":{"name":"Proceedings of the 12th International Symposium on Visual Information Communication and Interaction","volume":"138 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"How to Improve Semantics Understanding of Word Clouds\",\"authors\":\"Lu-Biao Yang, Jie Li, Wenhuan Lu, Yi Chen, Kang Zhang, Yan Li\",\"doi\":\"10.1145/3356422.3356449\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Word cloud is a text visualization technique which is widely applied in helping improve semantic understanding about target materials. One of the most important features is the font size, which represents words frequencies of a document. As the result, in this paper, we explore how to set font sizes of words, and its influence on semantic understanding through people's performance with qualitative and controlled experiments. Adopting an machine learning algorithm LDA (Latent Dirichlet Allocation) topic model, we quantify semantics of the document and judge participants' accuracy performance. The experimental results show the influence of different font size on semantic understanding performance and provide insights for ways in promoting semantic understanding of word cloud.\",\"PeriodicalId\":197051,\"journal\":{\"name\":\"Proceedings of the 12th International Symposium on Visual Information Communication and Interaction\",\"volume\":\"138 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 12th International Symposium on Visual Information Communication and Interaction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3356422.3356449\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th International Symposium on Visual Information Communication and Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3356422.3356449","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

词云是一种文本可视化技术,广泛应用于提高对目标材料的语义理解。最重要的特性之一是字体大小,它表示文档的单词频率。因此,在本文中,我们通过定性和对照实验,探讨了如何设置单词的字体大小,以及它对人们的语义理解的影响。采用机器学习算法LDA (Latent Dirichlet Allocation)主题模型,量化文档的语义并判断参与者的准确性表现。实验结果显示了不同字体大小对语义理解性能的影响,为促进词云的语义理解提供了思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How to Improve Semantics Understanding of Word Clouds
Word cloud is a text visualization technique which is widely applied in helping improve semantic understanding about target materials. One of the most important features is the font size, which represents words frequencies of a document. As the result, in this paper, we explore how to set font sizes of words, and its influence on semantic understanding through people's performance with qualitative and controlled experiments. Adopting an machine learning algorithm LDA (Latent Dirichlet Allocation) topic model, we quantify semantics of the document and judge participants' accuracy performance. The experimental results show the influence of different font size on semantic understanding performance and provide insights for ways in promoting semantic understanding of word cloud.
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
×
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