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Lots of wild operations happen in this next step that transform your
unstructured text to numeric time series objects aggregated by
conversation and interlocutor. It is important that you have a handle on
what prep_dyads() does and what processes such as
lemmatization and stopword removal mean.
prep_dyads() uses numerous regex to clean and format the
data your just read into R in the previous step.
ConversationAlign applies an ordered sequence of cleaning
steps on the road toward vectorizing your original text into a
one-word-per row format. These steps include: converting all text to
lowercase, expanding contractions, omitting all non-alphabetic
characters (e.g., numbers, punctuation, line breaks). In addition to
text cleaning, users guide options for stopword removal and
lemmatization. During formatting prep_dyads() will prompt
you to select up to three variables for computing alignment on. This
works by joining values from a large internal lookup database to each
word in your language transcript. prep_dyads() is
customizable via the following arguments.
There are two important arguments regarding stopword removal.
omit_stops specifies whether or not to remove stopwords.
which_stopwords specifies which stopword list you would
like to apply with the default being Temple_stops25. The
full list of choices is: none, SMART_stops,
CA_orig_stops. MIT_stops, and
Temple_stops25. Stopword removal is an important, yet also
controversial step in text cleaning.
ConversationAlign calls the textstem
package as a dependency to lemmatize your language transcript. This
converts morphologiocal derivatives to their root forms. The default is
lemmatize=T. Sometimes you want to retain language output in its native
form. If this is the case, change the argument in clean_dyads to
lemmatize=F. clean_dyads() outputs word count metrics
pre/post cleaning by dyad and interlocutor. This can be useful if you
are interested in whether one person just doesn’t produce many words or
produces a great deal of empty utterances.
This is where the magic happens. prep_dyads() will yoke
published norms for >40 possible dimensions to every content word in
your transcript (up to 3 at a time). This join is executed by merging
your vectorized conversation transcript with a huge internal lexical
database with norms spanning over 100k English words.
prep_dyads() will prompt you to select anywhere from 1 to 3
target dimensions at a time. Enter the number corresponding to each
dimension of interest separated by spaces and then hit enter (e.g., 10
14 19) ConversationAlign will append a published norm if
available (e.g., concreteness, word length) to every running word in
your transcript. These quantitative values are used in the subsequent
summarize_dyads() step to compute alignment.
prep_dyads()-Cleans, formats, and vectorizes conversation transwcripts to a
one-word-per-row format -Yokes psycholinguistic norms for up to three
dimensions at a time (from <40 possible dimensions) to each content
word. -Retains metadata
Arguments to
prep_dyads:
1) dat_read= name
of the dataframe created during read_dyads()
2)
omit_stops= T/F (default=T) option to remove stopwords
3) lemmatize= lemmatize strings converting each entry
to its dictionary form, default is lemmatize=TRUE
4)
which_stoplist= quoted argument specifying stopword
list, options include none, MIT_stops,
SMART, CA_OriginalStops, or
Temple_stops25. Default is Temple_stops25
#Example of running the function
NurseryRhymes_Prepped <- prep_dyads(dat_read=NurseryRhymes, lemmatize=TRUE, omit_stops=T, which_stoplist="Temple_stops25")This embedded as external data in the package with ‘anger’ values yoked to each word.
| Event_ID | Participant_ID | Exchange_Count | Turn_Count | Text_Prep | Text_Clean | emo_anger |
|---|---|---|---|---|---|---|
| ItsySpider | Yin | 1 | 1 | the | NA | NA |
| ItsySpider | Yin | 1 | 1 | itsy | itsy | -0.02 |
| ItsySpider | Yin | 1 | 1 | bitsy | bitsy | -0.02 |
| ItsySpider | Yin | 1 | 1 | spider | spider | 0.04 |
| ItsySpider | Yin | 1 | 1 | climbed | climb | -0.09 |
| ItsySpider | Yin | 1 | 1 | up | up | -0.06 |
| ItsySpider | Yin | 1 | 1 | the | NA | NA |
| ItsySpider | Yin | 1 | 1 | water | water | -0.17 |
| ItsySpider | Yin | 1 | 1 | spout | spout | 0.05 |
| ItsySpider | Maya | 1 | 2 | down | down | 0.03 |
| ItsySpider | Maya | 1 | 2 | came | come | -0.13 |
| ItsySpider | Maya | 1 | 2 | the | NA | NA |
| ItsySpider | Maya | 1 | 2 | rain | rain | 0.07 |
| ItsySpider | Maya | 1 | 2 | and | NA | NA |
| ItsySpider | Maya | 1 | 2 | washed | wash | 0.06 |
| ItsySpider | Maya | 1 | 2 | the | NA | NA |
| ItsySpider | Maya | 1 | 2 | spider | spider | 0.04 |
| ItsySpider | Maya | 1 | 2 | out | out | 0.04 |
| ItsySpider | Yin | 2 | 3 | out | out | 0.04 |
| ItsySpider | Yin | 2 | 3 | came | come | -0.13 |
These 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.