Paralanguage Classifier (PARA): An Algorithm for Automatic Coding of Paralinguistic Nonverbal Parts of Speech in Text.
| Title: | Paralanguage Classifier (PARA): An Algorithm for Automatic Coding of Paralinguistic Nonverbal Parts of Speech in Text. |
|---|---|
| Authors: | Luangrath, Andrea Webb (AUTHOR) andrea-luangrath@uiowa.edu, Xu, Yixiang (AUTHOR), Wang, Tong (AUTHOR) |
| Source: | Journal of Marketing Research (JMR). Apr2023, Vol. 60 Issue 2, p388-408. 21p. 9 Charts, 3 Graphs. |
| Subject Terms: | *Consumer behavior, *Customer relations, Paralinguistics, Text mining, Social media, Emoticons & emojis, Nonverbal communication, Word-of-mouth communication |
| Abstract: | Brands and consumers alike have become creators and distributors of digital words, thus generating increasing interest in insights to be gained from text-based content. This work develops an algorithm to identify textual paralanguage, defined as nonverbal parts of speech expressed in online communication. The authors develop and validate a paralanguage classifier (called PARA) using social media data from Twitter, YouTube, and Instagram (N = 1,241,489 posts). Using auditory, tactile, and visual properties of text, PARA detects nonverbal communication cues, aspects of text often neglected by other word-based sentiment lexica. This work is the first to reveal the importance of textual paralanguage as a critical indicator of sentiment valence and intensity. The authors further demonstrate that automatically detected textual paralanguage can predict consumer engagement above and beyond existing text analytics tools. The algorithm is designed for researchers, scholars, and practitioners seeking to optimize marketing communications and offers a methodological advancement to quantify the importance of not only what is said verbally but how it is said nonverbally. [ABSTRACT FROM AUTHOR] |
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| Database: | Business Source Complete |
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| Items | – Name: Title Label: Title Group: Ti Data: Paralanguage Classifier (PARA): An Algorithm for Automatic Coding of Paralinguistic Nonverbal Parts of Speech in Text. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Luangrath%2C+Andrea+Webb%22">Luangrath, Andrea Webb</searchLink> (AUTHOR)<i> andrea-luangrath@uiowa.edu</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Yixiang%22">Xu, Yixiang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Tong%22">Wang, Tong</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Marketing+Research+%28JMR%29%22">Journal of Marketing Research (JMR)</searchLink>. Apr2023, Vol. 60 Issue 2, p388-408. 21p. 9 Charts, 3 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Consumer+behavior%22">Consumer behavior</searchLink><br />*<searchLink fieldCode="DE" term="%22Customer+relations%22">Customer relations</searchLink><br /><searchLink fieldCode="DE" term="%22Paralinguistics%22">Paralinguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Social+media%22">Social media</searchLink><br /><searchLink fieldCode="DE" term="%22Emoticons+%26+emojis%22">Emoticons & emojis</searchLink><br /><searchLink fieldCode="DE" term="%22Nonverbal+communication%22">Nonverbal communication</searchLink><br /><searchLink fieldCode="DE" term="%22Word-of-mouth+communication%22">Word-of-mouth communication</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Brands and consumers alike have become creators and distributors of digital words, thus generating increasing interest in insights to be gained from text-based content. This work develops an algorithm to identify textual paralanguage, defined as nonverbal parts of speech expressed in online communication. The authors develop and validate a paralanguage classifier (called PARA) using social media data from Twitter, YouTube, and Instagram (N = 1,241,489 posts). Using auditory, tactile, and visual properties of text, PARA detects nonverbal communication cues, aspects of text often neglected by other word-based sentiment lexica. This work is the first to reveal the importance of textual paralanguage as a critical indicator of sentiment valence and intensity. The authors further demonstrate that automatically detected textual paralanguage can predict consumer engagement above and beyond existing text analytics tools. The algorithm is designed for researchers, scholars, and practitioners seeking to optimize marketing communications and offers a methodological advancement to quantify the importance of not only what is said verbally but how it is said nonverbally. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Marketing Research (JMR) is the property of American Marketing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/00222437221116058 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 388 Subjects: – SubjectFull: Consumer behavior Type: general – SubjectFull: Customer relations Type: general – SubjectFull: Paralinguistics Type: general – SubjectFull: Text mining Type: general – SubjectFull: Social media Type: general – SubjectFull: Emoticons & emojis Type: general – SubjectFull: Nonverbal communication Type: general – SubjectFull: Word-of-mouth communication Type: general Titles: – TitleFull: Paralanguage Classifier (PARA): An Algorithm for Automatic Coding of Paralinguistic Nonverbal Parts of Speech in Text. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Luangrath, Andrea Webb – PersonEntity: Name: NameFull: Xu, Yixiang – PersonEntity: Name: NameFull: Wang, Tong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00222437 Numbering: – Type: volume Value: 60 – Type: issue Value: 2 Titles: – TitleFull: Journal of Marketing Research (JMR) Type: main |
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