文章基本信息

标题：Detecting a Tweet's Topic within a Large Number of Portuguese Twitter Trends
本地全文：下载
作者：Hugo Rosa ; Jo{\~a}o Paulo Carvalho ; Fernando Batista 等
期刊名称：OASIcs : OpenAccess Series in Informatics
电子版ISSN：2190-6807
出版年度：2014
卷号：38
页码：185-199
DOI：10.4230/OASIcs.SLATE.2014.185
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：In this paper we propose to approach the subject of Twitter Topic Detection when in the presence of a large number of trending topics. We use a new technique, called Twitter Topic Fuzzy Fingerprints, and compare it with two popular text classification techniques, Support Vector Machines (SVM) and k-Nearest Neighbours (kNN). Preliminary results show that it outperforms the other two techniques, while still being much faster, which is an essential feature when processing large volumes of streaming data. We focused on a data set of Portuguese language tweets and the respective top trends as indicated by Twitter.
关键词：topic detection; social networks data mining; Twitter; Portuguese language