A Context-Dependent Supervised Learning Approach to Sentiment Detection in Large Textual Databases

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 Na

• Type: Journal
• Year: 2010
• Where: JIDM
• Authors: Albert Weichselbraun, Stefan Gindl, Arno Scharl
• Keywords: Ambiguous Terms, JIDM 2010, Most Sentiment Detection, Sentiment Detection