Volltext-Downloads (blau) und Frontdoor-Views (grau)
  • search hit 10 of 27
Back to Result List

Predictive Features for Detecting Indefinite Polar Sentences

  • 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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author:Michael WiegandGND, Dietrich Klakow
URN:urn:nbn:de:bsz:mh39-85052
URL:https://aclanthology.info/papers/L10-1250/l10-1250
ISBN:2-9517408-6-7
Parent Title (English):Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), May 17-23, 2010, Valletta, Malta
Publisher:European Language Resources Association
Place of publication:Paris
Editor:Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, Daniel Tapias
Document Type:Conference Proceeding
Language:English
Year of first Publication:2010
Date of Publication (online):2019/02/21
Publicationstate:Zweitveröffentlichung
Reviewstate:Peer-Review
Tag:Document Classification; Information Extraction; Information Retrieval; Semantics; Text Categorisation
GND Keyword:Computerlinguistik; Information Extraction; Maschinelles Lernen; Natürliche Sprache; Polarität
First Page:3092
Last Page:3096
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Linguistics-Classification:Computerlinguistik
Licence (German):License LogoCreative Commons - Namensnennung 4.0 International