Authors
Albert Weichselbraun, Stefan Gindl, Arno Scharl
Publication date
2014/10/1
Journal
Knowledge-based systems
Volume
69
Pages
78-85
Publisher
Elsevier
Description
This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept …
Scholar articles
A Weichselbraun, S Gindl, A Scharl - Knowledge-based systems, 2014
A Weichselbraun, S Gindl, A Scharl - 2014