Automating Content Analysis of Open-Ended Responses: and .

This study presents automated methods for predicting valence and quantifying valenced thoughts of a text. First, it examines whether , 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, termed . Results show that successfully predicts the valence of brief and grammatically imperfect open-ended responses, and achieves comparable performance to human coders when estimating number of valenced thoughts. Both and have promise as reliable, effective, and efficient methods when researchers content-analyze large amounts of textual data systematically.