title: Automating Content Analysis of Open-Ended Responses:WordscoresandAffective Intonation.
abstract: This study presents automated methods for predicting valence and quantifying valenced thoughts of a text. First, it examines whetherWordscores, developed by Laver, Benoit, and Garry (2003), can be adapted to reliably predict the valence of open-ended responses in a survey about bioethical issues in genetics research, and then tests a complementary and novel technique for coding the number of valenced thoughts in open-ended responses, termedAffective Intonation. Results show thatWordscoressuccessfully predicts the valence of brief and grammatically imperfect open-ended responses, andAffective Intonationachieves comparable performance to human coders when estimating number of valenced thoughts. BothWordscoresandAffective Intonationhave promise as reliable, effective, and efficient methods when researchers content-analyze large amounts of textual data systematically.
authors.affiliation: University of Pennsylvania, Annenberg School for Communication.
authors.forename: Young Min
authors.initials: YM
authors.lastname: Baek
authors.affiliation: University of Pennsylvania, Annenberg School for Communication.
authors.forename: Joseph N
authors.initials: JN
authors.lastname: Cappella
authors.affiliation: University of Pennsylvania, Annenberg School for Communication.
authors.forename: Alyssa
authors.initials: A
authors.lastname: Bindman