THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT

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...

Full description

Saved in:
Bibliographic Details
Published in:Computational intelligence 2006-05, Vol.22 (2), p.100-109
Main Authors: Koppel, Moshe, Schler, Jonathan
Format: Article
Language:English
Subjects:
Artificial intelligence
Classification
Emotions
Information technology
machine learning
sentiment analysis
Studies
text categorization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0824-7935
1467-8640
DOI:10.1111/j.1467-8640.2006.00276.x