Baek Young Min, Cappella Joseph N, Bindman Alyssa
University of Pennsylvania, Annenberg School for Communication.
Commun Methods Meas. 2011 Dec;5(4):275-296. doi: 10.1080/19312458.2011.624489.
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.
本研究提出了用于预测文本效价和量化文本中带有效价思想的自动化方法。首先，研究考察了由Laver、Benoit和Garry（2003年）开发的[方法名称未给出]能否适用于可靠地预测关于遗传学研究中生物伦理问题的调查中开放式回答的效价，然后测试一种用于对开放式回答中带有效价思想的数量进行编码的补充性新技术，称为[技术名称未给出]。结果表明，[方法名称未给出]成功地预测了简短且语法不完善的开放式回答的效价，并且在估计带有效价思想的数量时，[技术名称未给出]取得了与人工编码相当的表现。当研究人员系统地对大量文本数据进行内容分析时，[方法名称未给出]和[技术名称未给出]都有望成为可靠、有效且高效的方法。
