Good news or bad news? - Let the market decide

被引：0
|
作者：
Koppel, M [1 ]
Shtrimberg, I [1 ]
机构：
[1] Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel
来源：
COMPUTING ATTITUDE AND AFFECT IN TEXT: THEORY AND APPLICATIONS | 2006年 / 20卷
关键词：
sentiment analysis; financial analysis; automated labelling;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A simple and novel method for generating labeled examples for sentiment analysis is introduced: news stories about publicly traded companies are labeled positive or negative according to price changes of the company stock. It is shown that there are many lexical markers for bad news but none for good news. Overall, teamed models based on lexical features can distinguish good news from bad news with accuracy of about 70%. Unfortunately, this result does not yield profits since it works only when stories are labeled according to cotemporaneous price changes but does not work when they are labeled according to subsequent price changes.
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页码：297 / +
页数：3
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