TY - JOUR AU - Raaijmakers, Stephan AU - Kraaij, Wessel PY - 2010/05/16 Y2 - 2024/03/28 TI - Classifier Calibration for Multi-Domain Sentiment Classification JF - Proceedings of the International AAAI Conference on Web and Social Media JA - ICWSM VL - 4 IS - 1 SE - Poster Papers DO - 10.1609/icwsm.v4i1.14055 UR - https://ojs.aaai.org/index.php/ICWSM/article/view/14055 SP - 311-314 AB - <p> 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. </p> ER -