PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Importance of Neutral Examples for Learning Sentiment
Moshe Koppel and Jonathan Schler
Computational Intelligence Volume 22, Number 2, pp. 100-110, 2006.


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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:2681
Deposited By:Jonathan Schler
Deposited On:22 November 2006