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Using Text Analysis in Parallel Mediation Analysis
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DOI：10.1287/mksc.2023.0045.png)
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Abstract
En 中文
Text data are widely used in marketing research. In this paper, we propose a model that uses text data to identify multiple mediators in a parallel mediation analysis. Our model is based on the Latent Dirichlet Allocation (LDA) model that incorporates treatment and outcome variables. Treatment variables can affect topic composition in the text data, with topic probabilities used to predict outcomes via a logistic regression model. Lexical priors are introduced to seed topics that researchers consider relevant to an analysis, whereas non-seeded topics allow researchers to find other potential mediation paths. The resulting analysis of mediation replaces the use of rating scales with text that more flexibly reflects the reasons for respondent choices. The assessment of stimuli's effect on topic probabilities provides information on which aspects of stimuli contribute to the change in respondents' choices of words and their latent meanings behind these words.
Keywords:
topic modeling
lexical priors
semi-supervised LDA
machine learning
heterogeneous effects
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