Feature Selection for Twitter Classification

被引：7
|
作者：
Ostrowski, David Alfred [1 ]
机构：
[1] Ford Motor Co, Res & Innovat Ctr, Syst Analyt, Dearborn, MI 48121 USA
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC) | 2014年
关键词：
D O I：
10.1109/ICSC.2014.50
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Twitter-based messages have presented challenges in the identification of features as applied to classification. This paper explores filtering techniques for improved trend detection and information extraction. Starting with a pre-filtered source (Twitter), we will examine the application of both information theory and Natural Language Processing (NLP) based techniques as a means of preprocessing for classification. Results demonstrate that both means allow for improved results in classification among highly idiosyncratic data (Twitter).
引用
收藏
页码：267 / 272
页数：6
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