ANALYZING MEANING IN BIG DATA: PERFORMING A MAP ANALYSIS USING GRAMMATICAL PARSING AND TOPIC MODELING

Social scientists have recently started discussing the utilization of textmining tools as being fruitful for scaling inductively grounded close reading. We aim to progress in this direction and provide a contemporary contribution to the literature. By focusing on map analysis, we demonstrate the pot...

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Bibliographic Details
Published in:Sociological methodology 2019-08, Vol.49 (1), p.83-131
Main Authors: Goldenstein, Jan, Poschmann, Philipp
Format: Article
Language:English
Subjects:
Big Data
Communication
Data mining
Meaning
Social responsibility
Software
Symposium on Big Data-Innovations in Computational Social Science
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Summary:Social scientists have recently started discussing the utilization of textmining tools as being fruitful for scaling inductively grounded close reading. We aim to progress in this direction and provide a contemporary contribution to the literature. By focusing on map analysis, we demonstrate the potential of text-mining tools for text analysis that approaches inductive but still formal in-depth analysis. We propose that a combination of text-mining tools addressing different layers of meaning facilitates a closer analysis of the dynamics of manifest and latent meanings than is currently acknowledged. To illustrate our approach, we combine grammatical parsing and topic modeling to operationalize communication structures within sentences and the semantic surroundings of these communication structures. We use a reliable and downloadable software application to analyze the dynamic interlacement of two layers of meaning over time. We do so by analyzing 15,371 newspaper articles on corporate responsibility published in the United States from 1950 to 2013.
ISSN:0081-1750
1467-9531
DOI:10.1177/0081175019852762