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Enriching Semantic Knowledge Bases for Opinion Mining in Big Data Applications

Weichselbraun, Albert and Gindl, Stefan and Scharl, Arno (2014) Enriching Semantic Knowledge Bases for Opinion Mining in Big Data Applications. Knowledge-Based Systems, 69 . pp. 78-85. ISSN 0950-7051

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Official URL: http://dx.doi.org/10.1016/j.knosys.2014.04.039

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.

Item Type:Article
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
ID Code:78
Deposited By: Dr Albert Weichselbraun
Deposited On:03 Jun 2014 12:04
Last Modified:16 Oct 2014 09:55

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