This function provides the frequency of collocations in comments that correspond to the provided transcript, using fuzzy matching.
Usage
collocate_comments_fuzzy(
transcript_token,
note_token,
collocate_length = 5,
n_bands = 50,
threshold = 0.7,
n_gram_width = 4
)Arguments
- transcript_token
transcript token to act as baseline for notes, resulting from
token_transcript()- note_token
tokenized document of notes, resulting from
token_comments()- collocate_length
the length of the collocation. Default is 5
- n_bands
number of bands used in MinHash algorithm passed to
zoomerjoin::jaccard_right_join(). Default is 50- threshold
Jaccard distance threshold to be considered a match passed to
zoomerjoin::jaccard_right_join(). Default is 0.7- n_gram_width
width of n-grams used in Jaccard distance calculation passed to
zoomerjoin::jaccard_right_join(). Default is 4
Details
Collocations are sequences of words present in the source document. For example, the phrase "the blue bird flies" contains one collocation of length 4 ("the blue bird flies"), two collocations of length 3 ("the blue bird" and "blue bird flies"), and three collocations of length 2 ("the blue", "blue bird", and "bird flies"). This function counts the number of corresponding phrases in the 'notes', or the derivative documents. Due to fuzzy matching, indirect matches are included with a weight of (n*d)/m, where n is the frequency of the fuzzy collocation, d is the Jaccard similarity between the transcript and note collocation, and m is the number of closest matches for the note collocation.
Examples
# Rename relevant column to page_notes in the derivative document
comment_example_rename <- dplyr::rename(comment_example[1:10,], page_notes=Notes)
# Tokenize the derivative document
toks_comment <- token_comments(comment_example_rename)
# Rename relevant column in the source document to text
transcript_example_rename <- dplyr::rename(transcript_example, text=Text)
# Tokenize source document
toks_transcript <- token_transcript(transcript_example_rename)
# Compute collocation frequencies using fuzzy (or indirect) matching
fuzzy_object <- collocate_comments_fuzzy(toks_transcript, toks_comment)
#> Joining with `by = join_by(unlist.descript_ngrams.)`
#> Joining with `by = join_by(collocation)`
#> Joining with `by = join_by(collocation)`
#> Warning: A pair of records at the threshold (0.7) have only a 95% chance of being compared.
#> Please consider changing `n_bands` and `band_width`.
#> Joining with `by = join_by(collocation.y)`
#> Joining with `by = join_by(collocation)`
#> Joining with `by = join_by(word_number)`