Enriching semantic knowledge bases for opinion mining in big data applications

A. Weichselbraun, S. Gindl, A. Scharl
2014 Knowledge-Based Systems  
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
more » ... 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.
doi:10.1016/j.knosys.2014.04.039 pmid:25431524 pmcid:PMC4235782 fatcat:k7rcec6qeja6rkkjuou7z7g6si