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For our purposes a monologue is a ordered language sample that does
not contain turns and/or speaker information. Monologues consist of
narratives, stories, etc. An unordered list is a bag-of-words where word
order no longer matters. You might be interested in applying kmeans,
hierarchical clustering, or graph metrics to elucidate structure within
a list of words. Alternatively, you might be interested in evaluating
distances as ordered time series. Whatever your goal, the vignette to
follow will illustrate how to prep and clean your text data using
SemanticDistance.
Included in the package
| mytext |
|---|
| The girl walked down the street. The wrestler punched the boxer. I could not open the door. 95 dogs jumped on me. |
Included in the package
| mytext |
|---|
| trumpet trombone flute piano guitar gun knife missile bullet spear apple banana tomato sad angry happy disgusted |
clean_monologue_or_listTransforms all text to lowercase then optionally cleans (omit
stopwords, omit non-alphabetic chars), lemmatizes (transforms
morphological derivatives of words to their standard dictionary
entries), and splits multiword utterances into a one-word-per row
format. You can generally leave split_strings in its default state
(TRUE). clean_monologue appends several new variables to
your original dataframe: id_row_orig a numeric
identifier marking the original row where a word or group of words
appeared; ’id_row_postsplit a unique identifier marking
each word’s ordered position in the dataframe after splitting multiword
utterances across rows; word_clean result of all
cleaning operations, needed for distance calculations.
Function Arguments:
dat raw dataframe with at least one column of text
wordcol quoted variable column name where your target text
lives (e.g., ‘mytext’)
omit_stops omits stopwords, T/F
default is TRUE
lemmatize transforms raw word to
lemmatized form, T/F default is TRUE
Monologue_Cleaned <- clean_monologue_or_list(dat=Monologue_Typical, wordcol='mytext', omit_stops=TRUE, lemmatize=TRUE)
knitr::kable(head(Monologue_Cleaned, 12), format = "pipe", digits=2)| id_row_orig | text_initialsplit | word_clean | id_row_postsplit |
|---|---|---|---|
| 1 | the | NA | 1 |
| 1 | girl | girl | 2 |
| 1 | walked | walk | 3 |
| 1 | down | down | 4 |
| 1 | the | NA | 5 |
| 1 | street. | street | 6 |
| 1 | the | NA | 7 |
| 1 | wrestler | wrestler | 8 |
| 1 | punched | punch | 9 |
| 1 | the | NA | 10 |
| 1 | boxer. | boxer | 11 |
| 1 | i | NA | 12 |
dist_ngram2wordComputes cosine distance for two models (embedding and experiential)
using a rolling ngram approach consisting of groups of words (ngrams) to
the next word. IMPORTANT the function looks backward from the
target word skipping over NAs until filling the desired ngram size.
Function Arguments:
dat dataframe of a monologue transcript cleaned and prepped
with clean_monologue fn
ngram window size preceding
each new content word, ngram=1 means each word is compared to the word
before it.
Ngram2Word_Dists1 <- dist_ngram2word(dat=Monologue_Cleaned, ngram=1) #distance word-to-word
head(Ngram2Word_Dists1)
#> # A tibble: 6 × 6
#> id_row_orig text_initialsplit word_clean id_row_postsplit CosDist_1gram_glo
#> <fct> <chr> <chr> <int> <dbl>
#> 1 1 the <NA> 1 NA
#> 2 1 girl girl 2 NA
#> 3 1 walked walk 3 0.470
#> 4 1 down down 4 0.283
#> 5 1 the <NA> 5 NA
#> 6 1 street. street 6 0.362
#> # ℹ 1 more variable: CosDist_1gram_sd15 <dbl>dist_ngram2ngramUser specifies n-gram size (e.g., ngram=2). Distance computed from
each two-word chunk to the next iterating all the way down the dataframe
until there are no more words to ‘fill out’ the last ngram. Note this
distance function only works on monologue transcripts
where there are no speakers delineated and word order matters.
Function Arguments:
dat dataframe w/ a monologue sample cleaned and prepped
ngram chunk size (chunk-to-chunk), in this case
ngram=2 means chunks of 2 words compared to the next chunk
Ngram2Ngram_Dist1 <- dist_ngram2ngram(dat=Monologue_Cleaned, ngram=2)
head(Ngram2Ngram_Dist1)
#> # A tibble: 6 × 6
#> id_row_orig text_initialsplit word_clean id_row_postsplit CosDist_2gram_GLO
#> <fct> <chr> <chr> <int> <dbl>
#> 1 1 the <NA> 1 NA
#> 2 1 girl girl 2 NA
#> 3 1 walked walk 3 NA
#> 4 1 down down 4 0.141
#> 5 1 the <NA> 5 0.0608
#> 6 1 street. street 6 0.319
#> # ℹ 1 more variable: CosDist_2gram_SD15 <dbl>dist_anchorModels semantic distance from each successive new word to the average
of the semantic vectors for the first block of N content words. This
anchored distance provides a metric of overall semantic drift as a
language sample unfolds relative to a fixed starting point.
Function Arguments:
dat dataframe monologue sample cleaned and prepped using
‘clean_monologue’
anchor_size = size of the initial
chunk of words for chunk-to-new-word comparisons fn
Anchored_Dists1 <- dist_anchor(dat=Monologue_Cleaned, anchor_size=4)
head(Anchored_Dists1)
#> # A tibble: 6 × 4
#> id_row_postsplit word_clean CosDist_Anchor_GLO CosDist_Anchor_SD15
#> <int> <chr> <dbl> <dbl>
#> 1 1 <NA> NA NA
#> 2 2 girl 0.164 0.433
#> 3 3 walk 0.112 0.167
#> 4 4 down 0.0822 0.206
#> 5 5 <NA> NA NA
#> 6 6 street 0.259 0.170These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.