THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT

Authors: Koppel, Moshe¹; Schler, Jonathan¹

Source: Computational Intelligence, Volume 22, Number 2, May 2006 , pp. 100-109(10)

Publisher: Blackwell Publishing

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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: sentiment analysis; text categorization; machine learning

Document Type: Research article

DOI: 10.1111/j.1467-8640.2006.00276.x

Affiliations: 1: Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel

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