The importance of neutral examples for learning sentiment

被引：79
|
作者：
Koppel, Moshe [1 ]
Schler, Jonathan [1 ]
机构：
[1] Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel
来源：
COMPUTATIONAL INTELLIGENCE | 2006年 / 22卷 / 02期
关键词：
sentiment analysis; text categorization; machine learning;
D O I：
10.1111/j.1467-8640.2006.00276.x
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Most research on learning to identify sentiment ignores "neutral" examples, learning only from examples of significant (positive or negative) polarity. We show that it is crucial to use neutral examples in learning polarity for a variety of reasons. Learning from negative and positive examples alone will not permit accurate classification of neutral examples. Moreover, the use of neutral training examples in learning facilitates better distinction between positive and negative examples.
引用
收藏
页码：100 / 109
页数：10
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