Paper: Reducing Over-Weighting in Supervised Term Weighting for Sentiment Analysis

ACL ID C14-1125
Title Reducing Over-Weighting in Supervised Term Weighting for Sentiment Analysis
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

Recently the research on supervised term weighting has attracted growing attention in the field of Tradi- tional Text Categorization (TTC) and Sentiment Analysis (SA). Despite their impressive achievements, we show that existing methods more or less suffer from the problem of over-weighting. Overlooked by prior studies, over-weighting is a new concept proposed in this paper. To address this problem, two regularization techniques, singular term cutting and bias term, are integrated into our framework of su- pervised term weighting schemes. Using the concepts of over-weighting and regularization, we provide new insights into existing methods and present their regularized versions. Moreover, under the guidance of our framework, we develop a novel supervised term weighting scheme, regular...