Predictive Features for Detecting Indefinite Polar Sentences
Michael Wiegand and Michael Wiegand
In: LREC 2010, 19 May - 21 May 2010, La Valletta, Malta.
In recent years, text classification in sentiment analysis has mostly focused on two types of classification, the distinction between objective
and subjective text, i.e. subjectivity detection, and the distinction between positive and negative subjective text, i.e. polarity classification.
So far, there has been little work examining the distinction between definite polar subjectivity and indefinite polar subjectivity. While
the former are utterances which can be categorized as either positive or negative, the latter cannot be categorized as either of these two
categories. This paper presents a small set of domain independent features to detect indefinite polar sentences. The features reflect the
linguistic structure underlying these types of utterances. We give evidence for the effectiveness of these features by incorporating them
into an unsupervised rule-based classifier for sentence-level analysis and compare its performance with supervised machine learning
classifiers, i.e. Support Vector Machines (SVMs) and Nearest Neighbor Classifier (kNN). The data used for the experiments are webreviews
collected from three different domains.