TY - JOUR SN - 0950-7051 AU - Weichselbraun, Albert AU - Gindl, Stefan AU - Scharl, Arno T1 - Enriching semantic knowledge bases for opinion mining in big data applications JF - Knowledge-based systems SP - 78 EP - 85 VL - 69 PY - 2014 U3 - Journal Article UR - doi.org/10.1016/j.knosys.2014.04.039 AB - 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. LA - eng TS - PubMed Y3 - 18.05.2021 AD - Faculty of Information Science, University of Applied Sciences Chur, Pulvermühlestrasse 57, CH-7004 Chur, Switzerland. Department of New Media Technology, MODUL University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria. Department of New Media Technology, MODUL University Vienna, Am Kahlenberg 1, 1190 Vienna, Austria. M4 - Citavi ER -