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A Low Effort Approach to Quantitative Content Analysis

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Knowledge Engineering and Semantic Web (KESW 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 518))

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Abstract

We propose a workflow for an individual sociologist to be able to use quantitative content analysis in small-scale short-term research projects. The key idea of the approach is to generate a domain-oriented dictionary for researchers with limited resources. The workflow starts like a typical one and then deviates to include content analysis. First, the researcher performs deductive analysis which results in an interview guide. Second, the researcher conducts the small number of interviews to collect a domain-oriented labelled text corpus. Third, a domain-oriented dictionary is generated for the following content analysis. We propose and compare a number of methods to automatically extract a domain-oriented dictionary from a labelled corpus. Some properties of the proposed workflow are empirically studied based on a sociological research on volunteering in Russia.

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Authors and Affiliations

  1. Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 2nd Educational Building, CMC Faculty, MSU, Leninskie Gory, GSP-1, Moscow, 119991, Russia

    Maria Saburova & Archil Maysuradze

Authors
  1. Maria Saburova
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  2. Archil Maysuradze
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Corresponding author

Correspondence to Maria Saburova .

Editor information

Editors and Affiliations

  1. Complexible Inc, Washington, District of Columbia, USA

    Pavel Klinov

  2. ITMO University, St. Petersburg, Russia

    Dmitry Mouromtsev

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Saburova, M., Maysuradze, A. (2015). A Low Effort Approach to Quantitative Content Analysis. In: Klinov, P., Mouromtsev, D. (eds) Knowledge Engineering and Semantic Web. KESW 2015. Communications in Computer and Information Science, vol 518. Springer, Cham. https://doi.org/10.1007/978-3-319-24543-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-24543-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24542-3

  • Online ISBN: 978-3-319-24543-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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