doc2concrete {doc2concrete}

Concreteness Scores

Description

Detects linguistic markers of concreteness in natural language. This function is the workhorse of the doc2concrete package, taking a vector of text documents and returning an equal-length vector of concreteness scores.

Usage

doc2concrete(
  texts,
  domain = c("open", "advice", "plans"),
  wordlist = doc2concrete::mturk_list,
  stop.words = TRUE,
  number.words = TRUE,
  shrink = FALSE,
  fill = FALSE,
  uk_english = FALSE,
  num.mc.cores = 1
)

Arguments

texts

character A vector of texts, each of which will be tallied for concreteness.

domain

character Indicates the domain from which the text data was collected (see details).

wordlist

Dictionary to be used. Default is the Brysbaert et al. (2014) list.

stop.words

logical Should stop words be kept? Default is TRUE

number.words

logical Should numbers be converted to words? Default is TRUE

shrink

logical Should open-domain concreteness models regularize low-count words? Default is FALSE.

fill

logical Should empty cells be assigned the mean rating? Default is TRUE.

uk_english

logical Does the text contain any British English spelling? Including variants (e.g. Canadian). Default is FALSE

num.mc.cores

numeric number of cores for parallel processing - see parallel::detectCores(). Default is 1.

Details

In principle, concreteness could be measured from any english text. However, the definition and interpretation of concreteness may vary based on the domain. Here, we provide a domain-specific pre-trained classifier for concreteness in advice & feedback data, which we have empirically confirmed to be robust across a variety of contexts within that domain (Yeomans, 2021).

The training data for the advice classifier includes both second-person (e.g. "You should") and third-person (e.g. "She should") framing, including some names (e.g. "Riley should"). For consistency, we anonymised all our training data to replace any names with "Riley". If you are working with a dataset that includes the names of advice recipients, we recommend you convert all those names to "Riley" as well, to ensure optimal performance of the algorithm (and to respect their privacy).

There are many domains where such pre-training is not yet possible. Accordingly, we provide support for two off-the-shelf concreteness "dictionaries" - i.e. document-level aggregations of word-level scores. We found that that have modest (but consistent) accuracy across domains and contexts. However, we still encourage researchers to train a model of concreteness in their own domain, if possible.

Value

A vector of concreteness scores, with one value for every item in 'text'.

References

Yeomans, M. (2021). A Concrete Application of Open Science for Natural Language Processing. Organizational Behavior and Human Decision Processes, 162, 81-94.

Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904-911.

Paetzold, G., & Specia, L. (2016, June). Inferring psycholinguistic properties of words. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 435-440).

Examples

data("feedback_dat")

doc2concrete(feedback_dat$feedback, domain="open")


cor(doc2concrete(feedback_dat$feedback, domain="open"),feedback_dat$concrete)