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Qualitative Big Data Research? Integrating Natural Language Processing With Textual Analysis

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Abstract:

As Big Data research becomes more pervasive, so does the criticism of its epistemological, methodological and ethical/normative inadequacies. This paper proposes a hybrid approach to Big Data research that combines the scale of computational methods with the depth of qualitative analysis. It makes use of the simplest natural language processing algorithms to extract a purposive sample from a large textual corpus, which can then be analyzed using interpretive techniques. This helps research become more theoretically grounded and contextually sensitive—two major failings of extant Big Data studies. At the same time, it allows qualitative scholars to study datasets that are otherwise too large to examine manually as well as bring more rigor to the process of sampling—a long-standing weakness of qualitative research. The hybrid approach is illustrated with a study of the Inaugural Address Corpus, which includes the inaugural addresses of all US Presidents through history.
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Association:
Name: International Communication Association 65th Annual Conference
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http://www.icahdq.org


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URL: http://citation.allacademic.com/meta/p984727_index.html
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MLA Citation:

Shahin, Saif. "Qualitative Big Data Research? Integrating Natural Language Processing With Textual Analysis" Paper presented at the annual meeting of the International Communication Association 65th Annual Conference, Caribe Hilton, San Juan, Puerto Rico, May 21, 2015 <Not Available>. 2015-12-02 <http://citation.allacademic.com/meta/p984727_index.html>

APA Citation:

Shahin, S. , 2015-05-21 "Qualitative Big Data Research? Integrating Natural Language Processing With Textual Analysis" Paper presented at the annual meeting of the International Communication Association 65th Annual Conference, Caribe Hilton, San Juan, Puerto Rico Online <APPLICATION/PDF>. 2015-12-02 from http://citation.allacademic.com/meta/p984727_index.html

Publication Type: Session Paper
Review Method: Peer Reviewed
Abstract: As Big Data research becomes more pervasive, so does the criticism of its epistemological, methodological and ethical/normative inadequacies. This paper proposes a hybrid approach to Big Data research that combines the scale of computational methods with the depth of qualitative analysis. It makes use of the simplest natural language processing algorithms to extract a purposive sample from a large textual corpus, which can then be analyzed using interpretive techniques. This helps research become more theoretically grounded and contextually sensitive—two major failings of extant Big Data studies. At the same time, it allows qualitative scholars to study datasets that are otherwise too large to examine manually as well as bring more rigor to the process of sampling—a long-standing weakness of qualitative research. The hybrid approach is illustrated with a study of the Inaugural Address Corpus, which includes the inaugural addresses of all US Presidents through history.


Similar Titles:
Linguistic Analysis: The Study of Textual Data in Social Science with Natural Language Processing


 
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