Content Analysis of E-petitions with Topic Modeling: How to Train and Evaluate LDA Models?
Document Type
Article
Publication Date
2018
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.ipm.2018.05.006
Abstract
E-petitions have become a popular vehicle for political activism, but studying them has been difficult because efficient methods for analyzing their content are currently lacking. Researchers have used topic modeling for content analysis, but current practices carry some serious limitations. While modeling may be more efficient than manually reading each petition, it generally relies on unsupervised machine learning and so requires a dependable training and validation process. And so this paper describes a framework to train and validate Latent Dirichlet Allocation (LDA), the simplest and most popular topic modeling algorithm, using e-petition data. With rigorous training and evaluation, 87% of LDA-generated topics made sense to human judges. Topics also aligned well with results from an independent content analysis by the Pew Research Center, and were strongly associated with corresponding social events. Computer-assisted content analysts can benefit from our guidelines to supervise every process of training and evaluation of LDA. Software developers can benefit from learning the demands of social scientists when using LDA for content analysis. These findings have significant implications for developing LDA tools and assuring validity and interpretability of LDA content analysis. In addition, LDA topics can have some advantages over subjects extracted by manual content analysis by reflecting multiple themes expressed in texts, by extracting new themes that are not highlighted by human coders, and by being less prone to human bias.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Information Processing & Management, v. 54, issue 6, p. 1292-1307
Scholar Commons Citation
Hagen, Loni, "Content Analysis of E-petitions with Topic Modeling: How to Train and Evaluate LDA Models?" (2018). School of Information Faculty Publications. 632.
https://digitalcommons.usf.edu/si_facpub/632
