Enriching Semantic Knowledge Bases for Opinion Mining in Big Data Applications


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


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.

Keywords: Web intelligence; Social Web; Big data; Knowledge extraction; Opinion mining; Sentiment analysis; Disambiguation; Contextualization; Common-sense knowledge; Concept grounding

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