一种用于tweet分类的短消息分类算法

P. Selvaperumal, A. Suruliandi
{"title":"一种用于tweet分类的短消息分类算法","authors":"P. Selvaperumal, A. Suruliandi","doi":"10.1109/ICRTIT.2014.6996189","DOIUrl":null,"url":null,"abstract":"Twitter users tweet their views in the form of short text messages. Twitter topic classification is classifying the tweets in to a set of predefined classes. In this work, a new tweet classification Method that makes use of tweet features like URL's in the tweet, retweeted tweets and influential users tweet is proposed. Experiments were carried out with extensive tweet data set. The performance of the proposed algorithm in classifying the tweets was compared with the text classification algorithms like SVM, Naïve Bayes, KNN etc. It is observed that the proposed method outclasses the conventional text classification algorithms in classifying the tweets.","PeriodicalId":422275,"journal":{"name":"2014 International Conference on Recent Trends in Information Technology","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"A short message classification algorithm for tweet classification\",\"authors\":\"P. Selvaperumal, A. Suruliandi\",\"doi\":\"10.1109/ICRTIT.2014.6996189\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Twitter users tweet their views in the form of short text messages. Twitter topic classification is classifying the tweets in to a set of predefined classes. In this work, a new tweet classification Method that makes use of tweet features like URL's in the tweet, retweeted tweets and influential users tweet is proposed. Experiments were carried out with extensive tweet data set. The performance of the proposed algorithm in classifying the tweets was compared with the text classification algorithms like SVM, Naïve Bayes, KNN etc. It is observed that the proposed method outclasses the conventional text classification algorithms in classifying the tweets.\",\"PeriodicalId\":422275,\"journal\":{\"name\":\"2014 International Conference on Recent Trends in Information Technology\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Recent Trends in Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRTIT.2014.6996189\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Recent Trends in Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRTIT.2014.6996189","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

摘要

推特用户以短信的形式发布他们的观点。Twitter主题分类是将tweet分类到一组预定义的类中。本文提出了一种新的推文分类方法,该方法利用推文、转发推文和有影响力用户推文中的URL等推文特征。在广泛的推文数据集上进行了实验。将本文算法与SVM、Naïve贝叶斯、KNN等文本分类算法在推文分类中的性能进行了比较。结果表明,该方法在推文分类方面优于传统的文本分类算法。
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
A short message classification algorithm for tweet classification
Twitter users tweet their views in the form of short text messages. Twitter topic classification is classifying the tweets in to a set of predefined classes. In this work, a new tweet classification Method that makes use of tweet features like URL's in the tweet, retweeted tweets and influential users tweet is proposed. Experiments were carried out with extensive tweet data set. The performance of the proposed algorithm in classifying the tweets was compared with the text classification algorithms like SVM, Naïve Bayes, KNN etc. It is observed that the proposed method outclasses the conventional text classification algorithms in classifying the tweets.
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
DigiCloud: Scrutinizing apt service for coping with confidential control over utility practice Effect of multi-word features on the hierarchical clustering of web documents Efficient fingerprint lookup using Prefix Indexing Tablet An image encryption using chaotic permutation and diffusion Efficient design of different forms of FIR filter
×
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