Fit an LDA topic model on open-ended text

Cleans the text item via clean_text_responses(), tokenises with the package's shared internal tokeniser (reused rather than a second tidytext-based tokeniser, so LDA and term_frequency() can never drift on cleaning/stop-word rules), casts the token counts to a document-term matrix with tidytext::cast_dtm(), and fits topicmodels::LDA().

Usage

sframe_run_topic_model_lda(data, roles, options, instrument)

Arguments

data

A data.frame of responses.

roles

A list with item, the text/textarea item id.

options

A list; k (topic count, default 4L – a demonstration value, not a recommendation; see vignette("text-analysis")'s topic- modelling section for topicmodels::perplexity()-based selection), seed (default 42L), stop_words (passed through to the tokeniser).

instrument

Optional sframe instrument, passed to clean_text_responses() for item-type validation.

Value

A runner-contract result list: test = "topic_model_lda", table (topic/term/beta/rank, top 10 terms per topic), fit (a runtime-only list holding the LDA model object and the document-row-to- respondent mapping needed by extract_quotes()), apa, prompt. On failure: list(test = "topic_model_lda", error = <message>).