Enriching semantic knowledge bases for opinion mining in big data applications

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 com...

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Bibliographic Details
Published in:Knowledge-based systems 2014-10, Vol.69, p.78-85
Main Authors: Weichselbraun, A., Gindl, S., Scharl, A.
Format: Article
Language:English
Subjects:
Big data
Common-sense knowledge
Concept grounding
Contextualization
Disambiguation
Knowledge extraction
Opinion mining
Sentiment analysis
Social Web
Web intelligence
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Summary: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.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2014.04.039