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© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 13. Juni 2017)
1Scharl, A. ; Hubmann-Haidvogel, A.H. ; Jones, A. ; Fischl, D. ; Kamolov, R. ; Weichselbraun, A. ; Rafelsberger, W.: Analyzing the public discourse on works of fiction : detection and visualization of emotion in online coverage about HBO's Game of Thrones.
In: Information processing and management. 52(2016) no.1, S.129-138.
Abstract: This paper presents a Web intelligence portal that captures and aggregates news and social media coverage about "Game of Thrones", an American drama television series created for the HBO television network based on George R.R. Martin's series of fantasy novels. The system collects content from the Web sites of Anglo-American news media as well as from four social media platforms: Twitter, Facebook, Google+ and YouTube. An interactive dashboard with trend charts and synchronized visual analytics components not only shows how often Game of Thrones events and characters are being mentioned by journalists and viewers, but also provides a real-time account of concepts that are being associated with the unfolding storyline and each new episode. Positive or negative sentiment is computed automatically, which sheds light on the perception of actors and new plot elements.
Inhalt: Vgl.: doi:10.1016/j.ipm.2015.02.003.
Anmerkung: Beitrag in einem Themenheft "Emotion and sentiment in social and expressive media"
Themenfeld: Schöne Literatur
2Liu, W. ; Weichselbraun, A. ; Scharl, A. ; Chang, E.: Semi-automatic ontology extension using spreading activation.
In: Journal of universal knowledge management. 0(2005) no.1, S.50-58.
Abstract: This paper describes a system to semi-automatically extend and refine ontologies by mining textual data from the Web sites of international online media. Expanding a seed ontology creates a semantic network through co-occurrence analysis, trigger phrase analysis, and disambiguation based on the WordNet lexical dictionary. Spreading activation then processes this semantic network to find the most probable candidates for inclusion in an extended ontology. Approaches to identifying hierarchical relationships such as subsumption, head noun analysis and WordNet consultation are used to confirm and classify the found relationships. Using a seed ontology on "climate change" as an example, this paper demonstrates how spreading activation improves the result by naturally integrating the mentioned methods.
Themenfeld: Data Mining