topic-modelinglisted

# Topic Modeling for Survey and Experimental Text Data ## Instructions ### 1. Model Selection - Default to Structural Topic Models (STM) when analyzing text from surveys or experiments. STM incorporates document-level metadata — treatment conditions, respondent demographics, country — directly into estimation, allowing prevalence and content to vary with covariates (Roberts et al. 2014). - Use standard LDA only when no document-level covariates are needed and the corpus is large enough for unsupervised discovery (Blei, Ng & Jordan 2003). - Consider BERTopic when working with short texts where word co-occurrence is sparse, or when multilingual embedding-based similarity is required (Grootendorst 2022). BERTopic clusters document embeddings via HDBSCAN, which yields hard single-topic-per-document assignments (unlike STM/LDA mixed membership), c-TF-IDF topic words that can be unstable on small corpora, and no native covariate framework. Use it when embedding-based semantic similarity matters more than mixed-membership structure or covariate estimation. - Recognize the distinction between topic categorization and attitude inference: topic models reveal what respondents *discussed*, not what they *believe*. If the goal is inferring latent attitudes rather than categorizing surface content, complement topic modeling with an embeddings-based scaling of contextually common words or a separate supervised classifier (Hobbs & Green 2025; see also the companion `text-classification` s