THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT
Computational IntelligencePublished 1 May 2006Open access
Moshe Koppel, Jonathan Schler
Citations191
SJR quartileQ2
SJR score0.58
SNIP1.20
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TL;DR
It is shown that it is crucial to use neutral examples in learning polarity for a variety of reasons, and the use of neutral training examples inlearning facilitates better distinction between positive and negative examples.
Abstract
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.
Keywords
Computer Science
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