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- W2952343020 endingPage "131" @default.
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- W2952343020 abstract "Social scientists have recently started discussing the utilization of text-mining 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." @default.
- W2952343020 created "2019-06-27" @default.
- W2952343020 creator A5020167421 @default.
- W2952343020 creator A5027904457 @default.
- W2952343020 date "2019-06-18" @default.
- W2952343020 modified "2023-10-16" @default.
- W2952343020 title "Analyzing Meaning in Big Data: Performing a Map Analysis Using Grammatical Parsing and Topic Modeling" @default.
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- W2952343020 doi "https://doi.org/10.1177/0081175019852762" @default.
- W2952343020 hasPublicationYear "2019" @default.
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