Text Mining and Digital Humanities: Quantitative Analysis of African American Poetry

Hdl Handle:
http://hdl.handle.net/10675.2/565703
Title:
Text Mining and Digital Humanities: Quantitative Analysis of African American Poetry
Authors:
Brown, Taylohr; Jenkins, Diamond; Quiller, Walter
Abstract:
“Text Mining and Digital Humanities: Quantitative Analysis of African American Poetry” uses quantitative and qualitative analysis to formulate research questions about African American poetry. In this project, we use text-mining software to determine whether distinctive word patterns can be used to quantify the characteristics of African American poetry. For the purposes of this study, we rejected the notion that Black poetry is defined as poetry written by black authors. Instead, we argue, the distinctions in black poems should be specific enough to be classified in a separate category from other kinds of literature such as American literature, and we assert the definition of black poetry should not reduce “blackness”- what we describe as the shared cultural traditions or practices of African Americans- to certain experiences or tropes such as the rural, folk black experience. We selected Langston Hughes and the Harlem Renaissance as the earliest historical point for our inquiry, and we used Margaret Walker, Gwendolyn Brooks, Maya Angelou, and Alice Walker, poets whom Hughes directly influenced, as comparisons. We created a text database of the collected poems of the five authors and assessed the frequency of words/phrases related to three main categories that recur in the scholarship of black poetry: memory, identity, and music. After running our text data through mining software and looking specifically for words coded as memory, identity, and music variables, we were able to support our initial claim that quantitative analysis can be used as to support qualitative assertions of black poetry as a distinct genre of American poetry.
Affiliation:
Pamplin College of Arts, Humanities, and Social Sciences
Issue Date:
7-Aug-2015
URI:
http://hdl.handle.net/10675.2/565703
Type:
Presentation
Description:
Poster presentation given at the 2015 CURS Summer Scholars Symposium
Sponsors:
Office of the Provost, VP for Academic and Faculty Affairs, Office of Research
Appears in Collections:
Summer Scholars Program

Full metadata record

DC FieldValue Language
dc.contributor.authorBrown, Taylohren
dc.contributor.authorJenkins, Diamonden
dc.contributor.authorQuiller, Walteren
dc.date.accessioned2015-08-07T00:10:01Zen
dc.date.available2015-08-07T00:10:01Zen
dc.date.issued2015-08-07en
dc.identifier.urihttp://hdl.handle.net/10675.2/565703en
dc.descriptionPoster presentation given at the 2015 CURS Summer Scholars Symposiumen
dc.description.abstract“Text Mining and Digital Humanities: Quantitative Analysis of African American Poetry” uses quantitative and qualitative analysis to formulate research questions about African American poetry. In this project, we use text-mining software to determine whether distinctive word patterns can be used to quantify the characteristics of African American poetry. For the purposes of this study, we rejected the notion that Black poetry is defined as poetry written by black authors. Instead, we argue, the distinctions in black poems should be specific enough to be classified in a separate category from other kinds of literature such as American literature, and we assert the definition of black poetry should not reduce “blackness”- what we describe as the shared cultural traditions or practices of African Americans- to certain experiences or tropes such as the rural, folk black experience. We selected Langston Hughes and the Harlem Renaissance as the earliest historical point for our inquiry, and we used Margaret Walker, Gwendolyn Brooks, Maya Angelou, and Alice Walker, poets whom Hughes directly influenced, as comparisons. We created a text database of the collected poems of the five authors and assessed the frequency of words/phrases related to three main categories that recur in the scholarship of black poetry: memory, identity, and music. After running our text data through mining software and looking specifically for words coded as memory, identity, and music variables, we were able to support our initial claim that quantitative analysis can be used as to support qualitative assertions of black poetry as a distinct genre of American poetry.en
dc.description.sponsorshipOffice of the Provost, VP for Academic and Faculty Affairs, Office of Researchen
dc.subjectPoetryen
dc.subjectText Miningen
dc.subjectDigital Humanitiesen
dc.titleText Mining and Digital Humanities: Quantitative Analysis of African American Poetryen
dc.typePresentationen
dc.contributor.departmentPamplin College of Arts, Humanities, and Social Sciencesen
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