title: Enriching semantic knowledge bases for opinion mining in big data applications.
abstract: 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 grounding, and on the quality of the enrichment process.
authors.affiliation: Faculty of Information Science, University of Applied Sciences Chur, Pulvermühlestrasse 57, CH-7004 Chur, Switzerland.
authors.forename: A
authors.initials: A
authors.lastname: Weichselbraun
authors.affiliation: Department of New Media Technology, MODUL University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria.
authors.forename: S
authors.initials: S
authors.lastname: Gindl
authors.affiliation: Department of New Media Technology, MODUL University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria.
authors.forename: A
authors.initials: A
authors.lastname: Scharl
keywords: Big data; Common-sense knowledge; Concept grounding; Contextualization; Disambiguation; Knowledge extraction; Opinion mining; Sentiment analysis; Social Web; Web intelligence