Quotation Weichselbraun, Albert, Gindl, Stefan, Scharl, Arno. 2010. A Context-Dependent Supervised Learning Approach to Sentiment Detection in Large Textual Databases. Journal of Information and Data Management 1 (3): 329-342.




Sentiment detection automatically identifies emotions in textual data. The increasing amount of emotive documents available in corporate databases and on the World Wide Web calls for automated methods to process this important source of knowledge. Sentiment detection draws attention from researchers and practitioners alike - to enrich business intelligence applications, for example, or to measure the impact of customer reviews on purchasing decisions. Most sentiment detection approaches do not consider language ambiguity, despite the fact that one and the same sentiment term might differ in polarity depending on the context, in which a statement is made. To address this shortcoming, this paper introduces a novel method that uses Naïve Bayes to identify ambiguous terms. A contextualized sentiment lexicon stores the polarity of these terms, together with a set of co-occurring context terms. A formal evaluation of the assigned polarities confirms that considering the usage context of ambiguous terms improves the accuracy of high-throughput sentiment detection methods. Such methods are a prerequisite for using sentiment as a metadata element in storage and distributed file-level intelligence applications, as well as in enterprise portals that provide a semantic repository of an organization's information assets.


Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Information and Data Management
Language English
Title A Context-Dependent Supervised Learning Approach to Sentiment Detection in Large Textual Databases
Volume 1
Number 3
Year 2010
Page from 329
Page to 342
Reviewed? Y
URL http://eprints.weblyzard.com/25/1/jidm%2Dpublished%2D1.pdf


RAVEN - Relation Analysis and Visualization for Enterprise Networks
Weichselbraun, Albert (Former researcher)
Scharl, Arno (Former researcher)
Gindl, Stefan (Modul University Vienna, Austria)
Institute for Data, Process and Knowledge Management (AE Polleres) (Details)
Institute for Information Systems and Society IN (Details)
Google Scholar: Search