prep_dyads: prep_dyads

In ConversationAlign: Process Text and Compute Linguistic Alignment in Conversation Transcripts

prep_dyads

Description

Cleans, vectorizes and appends lexical norms to all content words in a language corpus. User guides options for stopword removal and lemmatization. User selects up to three psycholinguistic dimensions to yoke norms on each content word in the original conversation transcript.

Usage

prep_dyads(
  dat_read,
  lemmatize = TRUE,
  omit_stops = TRUE,
  which_stoplist = "Temple_stops25",
  verbose = TRUE
)

Arguments

dat_read	dataframe produced from read_dyads() function
lemmatize	logical, should words be lemmatized (switched to base morphological form), default is TRUE
omit_stops	option to remove stopwords, default TRUE
which_stoplist	user-specified stopword removal method with options including "none", "SMART", "MIT_stops", "CA_OriginalStops", or "Temple_Stopwords25". "Temple_Stopwords25 is the default list
verbose	display detailed output such as error messages and progress (default is TRUE)

Value

dataframe with text cleaned and vectorized to a one word per-row format. Lexical norms and metadata are appended to each content word. Cleaned text appears under a new column called 'Text_Clean'. Any selected dimensions (e.g., word length) and metadata are also appended to each word along with speaker identity, turn, and Event_ID (conversation identifier).

ConversationAlign documentation built on Aug. 8, 2025, 7:22 p.m.