Qualitative and quantitative research in the humanities and social sciences: how natural language processing (NLP) can help

Roberto Franzosi (), Wenqin Dong and Yilin Dong
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Roberto Franzosi: Emory University
Wenqin Dong: Carnegie Mellon University
Yilin Dong: Carnegie Mellon University

Quality & Quantity: International Journal of Methodology, 2022, vol. 56, issue 4, No 42, 2781 pages

Abstract: Abstract The paper describes computational tools that can be of great help to both qualitative and quantitative scholars in the humanities and social sciences who deal with words as data. The Java and Python tools described provide computer-automated ways of performing useful tasks: 1. check the filenames well-formedness; 2. find user-defined characters in English language stories (e.g., social actors, i.e., individuals, groups, organizations; animals) (“find the character”) via WordNet; 3. aggregate words into higher-level aggregates (e.g., “talk,” “say,” “write” are all verbs of “communication”) (“find the ancestor”) via WordNet; 4. evaluate human-created summaries of events taken from multiple sources where key actors found in the sources may have been left out in the summaries (“find the missing character”) via Stanford CoreNLP POS and NER annotators; 5. list the documents in an event cluster where names or locations present close similarities (“check the character’s name tag”) using Levenshtein word/edit distance and Stanford CoreNLP NER annotator; 6. list documents categorized into the wrong event cluster (“find the intruder”) via Stanford CoreNLP POS and NER annotators; 7. classify loose documents into most-likely event clusters (“find the character’s home”) via Stanford CoreNLP POS and NER annotators or date matcher; 8. find similarities between documents (“find the plagiarist”) using Lucene. These tools of automatic data checking can be applied to ongoing projects or completed projects to check data reliability. The NLP tools are designed with “a fourth grader” in mind, a user with no computer science background. Some five thousand newspaper articles from a project on racial violence (Georgia 1875–1935) are used to show how the tools work. But the tools have much wider applicability to a variety of problems of interest to both qualitative and quantitative scholars who deal with text as data.

Keywords: Words as data; Research in humanities and social sciences; Social movements; Natural language processing; NLP; Computational linguistics (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11135-021-01235-2

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