. (2014). “Enriching Semantic Knowledge Bases for Opinion Mining in Big Data Applications”. Knowledge-Based Systems, 69:78-85. doi: 10.1016/j.knosys.2014.04.039.
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