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Tonkin, E.L.; Tourte, G.J.L.: Working with text. tools, techniques and approaches for text mining (2016)
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- Abstract
- What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining.
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Leydesdorff, L.; Persson, O.: Mapping the geography of science : distribution patterns and networks of relations among cities and institutes (2010)
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- Abstract
- Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.
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Sun, X.; Lin, H.: Topical community detection from mining user tagging behavior and interest (2013)
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- Abstract
- With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.
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Frické, M.: Big data and its epistemology (2015)
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- Abstract
- The article considers whether Big Data, in the form of data-driven science, will enable the discovery, or appraisal, of universal scientific theories, instrumentalist tools, or inductive inferences. It points out, initially, that such aspirations are similar to the now-discredited inductivist approach to science. On the positive side, Big Data may permit larger sample sizes, cheaper and more extensive testing of theories, and the continuous assessment of theories. On the negative side, data-driven science encourages passive data collection, as opposed to experimentation and testing, and hornswoggling ("unsound statistical fiddling"). The roles of theory and data in inductive algorithms, statistical modeling, and scientific discoveries are analyzed, and it is argued that theory is needed at every turn. Data-driven science is a chimera.
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Nohr, H.: Big Data im Lichte der EU-Datenschutz-Grundverordnung (2017)
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Winterhalter, C.: Licence to mine : ein Überblick über Rahmenbedingungen von Text and Data Mining und den aktuellen Stand der Diskussion (2016)
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Miao, Q.; Li, Q.; Zeng, D.: Fine-grained opinion mining by integrating multiple review sources (2010)
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Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010)
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Kipcic, O.; Cramer, C.: Wie Zeitungsinhalte Forschung und Entwicklung befördern (2017)
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Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014)
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Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010)
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Carter, D.; Sholler, D.: Data science on the ground : hype, criticism, and everyday work (2016)
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Drees, B.: Text und data mining : Herausforderungen und Möglichkeiten für Bibliotheken (2016)
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Loonus, Y.: Einsatzbereiche der KI und ihre Relevanz für Information Professionals (2017)
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