Trend sensing via Twitter

被引：5
|
作者：
Yilmaz, Yavuz Selim [1 ]
Bulut, Muhammed Fatih [1 ]
Akcora, Cuneyt Gurcan [2 ]
Bayir, Murat Ali [1 ]
Demirbas, Murat [1 ]
机构：
[1] SUNY Buffalo, Dept Comp Sci & Engn, Buffalo, NY 14260 USA
[2] Univ Insubria, Dipartimento Informat & Comunicaz, I-21100 Varese, Italy
来源：
INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING | 2013年 / 14卷 / 01期
关键词：
trend sensing; opinion mining; city-wide sensing; Twitter;
D O I：
10.1504/IJAHUC.2013.056271
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Due to its ever increasing popularity, Twitter has become a pervasive information outlet. In this paper, we present a passive sensing framework for identifying trends via Twitter. In our framework, we use a multi-dimensional corpus for fine-granularity sensing of trends, and employ both vector-space and set-space methods for achieving accuracy. We present two applications of our framework. The first one is sensing trends in public opinion by using an emotion-category corpus. The second application is sensing trends in location-types in a city by using a location-category corpus. Our experiments show that the proposed methods are able to determine changes in trends effectively in both application scenarios.
引用
收藏
页码：16 / 26
页数：11
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