B-TTDb: A Database of Turkish Tweets for Predicting the Top One Hundred Emojis

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on the Web Pub Date : 2024-07-24 DOI:10.1145/3681783
Y. Bi̇ti̇ri̇m
{"title":"B-TTDb: A Database of Turkish Tweets for Predicting the Top One Hundred Emojis","authors":"Y. Bi̇ti̇ri̇m","doi":"10.1145/3681783","DOIUrl":null,"url":null,"abstract":"Emoji prediction is an important research task that focuses on finding the most appropriate emoji(s) quickly and effortlessly for a specific text. Now that Turkish is on the list of the top 20 most spoken languages in the world and there are a considerable number of Turkish-speaking social media users, studying emoji prediction in Turkish holds significant value. In this study, a Turkish tweets database, named Bitirim's Turkish Tweets Database (B-TTDb), was constructed for academic and industrial studies based on the prediction of the top 100 emojis. B-TTDb consists of four datasets. The first dataset includes raw tweets, the second dataset is the organized version of the first dataset, the third dataset is the pre-processed version of the second dataset, and the last one is the organized version of the third dataset. The last one is the final version and it is named Bitirim's Dataset (B-D). It includes a total of 158,201 unique tweets belonging to the top 100 emoji classes. For database validation, experiments were conducted on B-D with popular machine learning algorithms for the top 10, 20, 50, and 100 emojis. This study could be considered as the first study that contributes to the literature by the first validated large database of Turkish tweets that includes such a large number of emojis. In addition, B-TTDb could be a basis as well as motivation for various further studies.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on the Web","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3681783","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Emoji prediction is an important research task that focuses on finding the most appropriate emoji(s) quickly and effortlessly for a specific text. Now that Turkish is on the list of the top 20 most spoken languages in the world and there are a considerable number of Turkish-speaking social media users, studying emoji prediction in Turkish holds significant value. In this study, a Turkish tweets database, named Bitirim's Turkish Tweets Database (B-TTDb), was constructed for academic and industrial studies based on the prediction of the top 100 emojis. B-TTDb consists of four datasets. The first dataset includes raw tweets, the second dataset is the organized version of the first dataset, the third dataset is the pre-processed version of the second dataset, and the last one is the organized version of the third dataset. The last one is the final version and it is named Bitirim's Dataset (B-D). It includes a total of 158,201 unique tweets belonging to the top 100 emoji classes. For database validation, experiments were conducted on B-D with popular machine learning algorithms for the top 10, 20, 50, and 100 emojis. This study could be considered as the first study that contributes to the literature by the first validated large database of Turkish tweets that includes such a large number of emojis. In addition, B-TTDb could be a basis as well as motivation for various further studies.
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
B-TTDb:用于预测百大表情符号的土耳其推文数据库
表情符号预测是一项重要的研究任务,其重点是为特定文本快速、轻松地找到最合适的表情符号。目前,土耳其语已跻身世界上使用人数最多的 20 种语言之列,而且有相当数量的土耳其语社交媒体用户,因此研究土耳其语中的表情符号预测具有重要价值。在本研究中,基于对前 100 个表情符号的预测,构建了一个土耳其语推文数据库,名为 Bitirim 的土耳其语推文数据库(B-TTDb),用于学术和工业研究。B-TTDb 由四个数据集组成。第一个数据集包括原始推文,第二个数据集是第一个数据集的整理版,第三个数据集是第二个数据集的预处理版,最后一个数据集是第三个数据集的整理版。最后一个是最终版本,被命名为 Bitirim 数据集(B-D)。该数据集共包含 158201 条属于前 100 个表情符号类别的独特推文。为了验证数据库,使用流行的机器学习算法在 B-D 上对前 10、20、50 和 100 个表情符号进行了实验。这项研究可以说是土耳其推文大型数据库的首次验证,其中包含了大量的表情符号,是对文献的首次贡献。此外,B-TTDb 可以作为各种进一步研究的基础和动力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
ACM Transactions on the Web
ACM Transactions on the Web 工程技术-计算机:软件工程
CiteScore
4.90
自引率
0.00%
发文量
26
审稿时长
7.5 months
期刊介绍: Transactions on the Web (TWEB) is a journal publishing refereed articles reporting the results of research on Web content, applications, use, and related enabling technologies. Topics in the scope of TWEB include but are not limited to the following: Browsers and Web Interfaces; Electronic Commerce; Electronic Publishing; Hypertext and Hypermedia; Semantic Web; Web Engineering; Web Services; and Service-Oriented Computing XML. In addition, papers addressing the intersection of the following broader technologies with the Web are also in scope: Accessibility; Business Services Education; Knowledge Management and Representation; Mobility and pervasive computing; Performance and scalability; Recommender systems; Searching, Indexing, Classification, Retrieval and Querying, Data Mining and Analysis; Security and Privacy; and User Interfaces. Papers discussing specific Web technologies, applications, content generation and management and use are within scope. Also, papers describing novel applications of the web as well as papers on the underlying technologies are welcome.
期刊最新文献
B-TTDb: A Database of Turkish Tweets for Predicting the Top One Hundred Emojis INCEPT: A Framework for Duplicate Posts Classification with Combined Text Representations DCDIMB: Dynamic Community-based Diversified Influence Maximization using Bridge Nodes Know their Customers: An Empirical Study of Online Account Enumeration Attacks Learning Dynamic Multimodal Network Slot Concepts from the Web for Forecasting Environmental, Social and Governance Ratings
×
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