@inproceedings{WiegandKlakow2019,
  author    = {Michael Wiegand and Dietrich Klakow},
  title     = {Predictive Features for Detecting Indefinite Polar Sentences},
  series = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), May 17-23, 2010, Valletta, Malta},
  editor    = {Nicoletta Calzolari and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association},
  address   = {Paris},
  isbn      = {2-9517408-6-7},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-85052},
  pages     = {3092 -- 3096},
  year      = {2019},
  abstract  = {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 web-reviews collected from three different domains.},
  language  = {en}
}