A framework for real-time semantic social media analysis |
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| Affiliation: | 1. School of Nursing, Newark, NJ, USA;2. School of Social Work, Newark, NJ, USA;3. School of Pharmacy, Newark, NJ, USA;1. INRIA, Sophia Antipolis, France;2. Accenture Labs, Dublin, Ireland;1. University of Oxford, Department of Computer Science, Wolfson Building, Parks Road, OX1 3QD, Oxford, UK;2. Siemens Corporate Technology, Siemens AG, Otto-Hahn-Ring 6, 81739, Munich, Germany;3. National and Kapodistrian University of Athens, Panepistimiopolis, Ilissia, 15784, Athens, Greece;4. University of Luebeck, Ratzeburger Allee 160, 23562, Lübeck, Germany;5. Department of Informatics, University of Oslo, Blindern, 0316, Oslo, Norway;6. NTNU –Norwegian University of Science and Technology, Teknologiveien 22, 2815, Gjøvik, Norway;7. Athens University of Economics and Business, 76 Patission Street, 10434, Athens, Greece;1. Department of Haematology and Wellcome and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom;;2. Haematopathology & Oncology Diagnostics Service, Department of Haematology Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom; and;3. Cambridge Blood and Stem Cell Biobank, University of Cambridge, United Kingdom;1. Programa de Pós-Graduação em Ecologia e Recursos Naturais, Universidade Federal de São Carlos, São Carlos, SP, 13565-905, Brazil;2. Laboratório de Estudos Paleobiológicos, Departamento de Biologia, Universidade Federal de São Carlos, Sorocaba, SP, 18052-780, Brazil;3. Laboratório de Paleontologia, Universidade de São Paulo, Ribeirão Preto, SP, 14040-900, Brazil;4. Center for Applied Isotope Studies, University of Georgia, Athens, GA 30602, USA;5. Laboratório de Ecologia e Geociências, Instituto Multidisciplinar em Saúde, Universidade Federal da Bahia – Campus Anísio Teixeira, Vitória da Conquista, BA, 45029-094, Brazil |
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| Abstract: | This paper presents a framework for collecting and analysing large volume social media content. The real-time analytics framework comprises semantic annotation, Linked Open Data, semantic search, and dynamic result aggregation components. In addition, exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices, term clouds, treemaps, and choropleths. There is also an interactive semantic search interface (Prospector), where users can save, refine, and analyse the results of semantic search queries over time. Practical use of the framework is exemplified through three case studies: a general scenario analysing tweets from UK politicians and the public’s response to them in the run up to the 2015 UK general election, an investigation of attitudes towards climate change expressed by these politicians and the public, via their engagement with environmental topics, and an analysis of public tweets leading up to the UK’s referendum on leaving the EU (Brexit) in 2016. The paper also presents a brief evaluation and discussion of some of the key text analysis components, which are specifically adapted to the domain and task, and demonstrate scalability and efficiency of our toolkit in the case studies. |
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| Keywords: | Natural Language Processing Semantic search Social media analysis Linked Open Data Semantic annotation Sentiment analysis |
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