Classifier Calibration for Multi-Domain Sentiment Classification


  • Stephan Raaijmakers TNO ICT, Delft, The Netherlands
  • Wessel Kraaij TNO ICT, Delft, The Netherlands


sentiment analysis, classifier calibration, domain transfer


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.




How to Cite

Raaijmakers, S., & Kraaij, W. (2010). Classifier Calibration for Multi-Domain Sentiment Classification. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 311-314. Retrieved from