Transverse subjectivity classification

Abstract

In this paper, we consider the problem of building models that have high subjectivity classification accuracy across domains. For that purpose, we present and evaluate new methods based on multi-view learning using both high-level (i.e. linguistic features for subjectivity detection) and lowlevel features (i.e. unigrams and bigrams). In particular, we show that multi-view learning, combining high-level and low-level features with adapted classifiers, can lead to improved results compared to one of the state-of-the-art algorithms called Stochastic Agreement Regularization. In particular, the experiments show that dividing the set of characteristics into three views returns the best results overall with accuracy across domains of 91.3% for the Class-Guided Multi-View Learning Algorithm, which combines both Linear Discriminant Analysis and Support Vector Machines.