AAAI Publications, Fourth International AAAI Conference on Weblogs and Social Media

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Classifier Calibration for Multi-Domain Sentiment Classification
Stephan Raaijmakers, Wessel Kraaij

Last modified: 2010-05-16


Textual sentiment classifiers classify texts into a fixed number of affective classes, such as positive, negative or neutral sentiment, or subjective versus objective information. It has been observed that sentiment classifiers suffer from a lack of generalization capability: a classifier trained on a certain domain generally performs worse on data from another domain. This phenomenon has been attributed to domain-specific affective vocabulary. In this paper, we propose a voting-based thresholding approach, which calibrates a number of existing single-domain classifiers with respect to sentiment data from a new domain. The approach presupposes only a small amount of annotated data from the new domain. We evaluate three criteria for estimating thresholds, and discuss the ramifications of these criteria for the trade-off between classifier performance and manual annotation effort.


sentiment analysis; classifier calibration; domain transfer

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