transforEmotion (version 0.1.5)

nlp_scores: Natural Language Processing Scores

Description

Natural Language Processing using word embeddings to compute semantic similarities (cosine; see costring) of text and specified classes

Usage

nlp_scores(
  text,
  classes,
  semantic_space = c("baroni", "cbow", "cbow_ukwac", "en100", "glove", "tasa"),
  preprocess = TRUE,
  remove_stop = TRUE,
  keep_in_env = TRUE,
  envir = 1
)

Value

Returns semantic distances for the text classes

Arguments

text

Character vector or list. Text in a vector or list data format

classes

Character vector. Classes to score the text

semantic_space

Character vector. The semantic space used to compute the distances between words (more than one allowed). Here's a list of the semantic spaces:

"baroni"

Combination of British National Corpus, ukWaC corpus, and a 2009 Wikipedia dump. Space created using continuous bag of words algorithm using a context window size of 11 words (5 left and right) and 400 dimensions. Best word2vec model according to Baroni, Dinu, & Kruszewski (2014)

"cbow"

Combination of British National Corpus, ukWaC corpus, and a 2009 Wikipedia dump. Space created using continuous bag of words algorithm with a context window size of 5 (2 left and right) and 300 dimensions

"cbow_ukwac"

ukWaC corpus with the continuous bag of words algorithm with a context window size of 5 (2 left and right) and 400 dimensions

"en100"

Combination of British National Corpus, ukWaC corpus, and a 2009 Wikipedia dump. 100,000 most frequent words. Uses moving window model with a size of 5 (2 to the left and right). Positive pointwise mutual information and singular value decomposition was used to reduce the space to 300 dimensions

"glove"

Wikipedia 2014 dump and Gigaword 5 with 400,000 words (300 dimensions). Uses co-occurrence of words in text documents (uses cosine similarity)

"tasa"

Latent Semantic Analysis space from TASA corpus all (300 dimensions).Uses co-occurrence of words in text documents (uses cosine similarity)

preprocess

Boolean. Should basic preprocessing be applied? Includes making lowercase, keeping only alphanumeric characters, removing escape characters, removing repeated characters, and removing white space. Defaults to TRUE

remove_stop

Boolean. Should stop_words be removed? Defaults to TRUE

keep_in_env

Boolean. Whether the classifier should be kept in your global environment. Defaults to TRUE. By keeping the classifier in your environment, you can skip re-loading the classifier every time you run this function. TRUE is recommended

envir

Numeric. Environment for the classifier to be saved for repeated use. Defaults to the global environment

Author

Alexander P. Christensen <[e-mail]>

References

Baroni, M., Dinu, G., & Kruszewski, G. (2014). Don't count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In Proceedings of the 52nd annual meting of the association for computational linguistics (pp. 238-247).

Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato's problem: The Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104, 211-240.

Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (pp. 1532-1543).

Examples

# Load data
data(neo_ipip_extraversion)

# Example text 
text <- neo_ipip_extraversion$friendliness[1:5]

if (FALSE) {
# GloVe
nlp_scores(
 text = text,
 classes = c(
   "friendly", "gregarious", "assertive",
   "active", "excitement", "cheerful"
 )
)

# Baroni
nlp_scores(
 text = text,
 classes = c(
   "friendly", "gregarious", "assertive",
   "active", "excitement", "cheerful"
 ),
 semantic_space = "baroni"
)
 
# CBOW
nlp_scores(
 text = text,
 classes = c(
   "friendly", "gregarious", "assertive",
   "active", "excitement", "cheerful"
 ),
 semantic_space = "cbow"
)

# CBOW + ukWaC
nlp_scores(
 text = text,
 classes = c(
   "friendly", "gregarious", "assertive",
   "active", "excitement", "cheerful"
 ),
 semantic_space = "cbow_ukwac"
)

# en100
nlp_scores(
 text = text,
 classes = c(
   "friendly", "gregarious", "assertive",
   "active", "excitement", "cheerful"
 ),
 semantic_space = "en100"
)

# tasa
nlp_scores(
 text = text,
 classes = c(
   "friendly", "gregarious", "assertive",
   "active", "excitement", "cheerful"
 ),
 semantic_space = "tasa"
)
}