textCentrality: Compute semantic similarity score between single words' word...

In text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

Compute semantic similarity score between single words' word embeddings and the aggregated word embedding of all words.

Description

Compute semantic similarity score between single words' word embeddings and the aggregated word embedding of all words.

Usage

textCentrality(
  words,
  word_embeddings,
  word_types_embeddings = word_types_embeddings_df,
  method = "cosine",
  aggregation = "mean",
  min_freq_words_test = 0
)

Arguments

words	(character) Word or text variable to be plotted.
word_embeddings	Word embeddings from textEmbed for the words to be plotted (i.e., the aggregated word embeddings for the "words" variable).
word_types_embeddings	Word embeddings from textEmbed for individual words (i.e., the decontextualized word embeddings).
method	(character) Character string describing type of measure to be computed. Default is "cosine" (see also "spearmen", "pearson" as well as measures from textDistance() (which here is computed as 1 - textDistance) including "euclidean", "maximum", "manhattan", "canberra", "binary" and "minkowski").
aggregation	(character) Method to aggregate the word embeddings (default = "mean"; see also "min", "max" or "[CLS]").
min_freq_words_test	(numeric) Option to select words that have at least occurred a specified number of times (default = 0); when creating the semantic similarity scores.

Value

A dataframe with variables (e.g., including semantic similarity, frequencies) for the individual words that are used as input for the plotting in the textCentralityPlot function.

See Also

See textCentralityPlot and textProjection.

Examples

# Computes the semantic similarity between the individual word embeddings (Iwe)
# in the "harmonywords" column of the pre-installed dataset: Language_based_assessment_data_8,
# and the aggregated word embedding (Awe).
# The Awe can be interpreted the latent meaning of the text.

## Not run: 
df_for_plotting <- textCentrality(
  words = Language_based_assessment_data_8["harmonywords"],
  word_embeddings = word_embeddings_4$texts$harmonywords,
  word_types_embeddings = word_embeddings_4$word_types
)

# df_for_plotting contain variables (e.g., semantic similarity, frequencies) for
# the individual words that are used for plotting by the textCentralityPlot function.

## End(Not run)

text documentation built on Sept. 11, 2024, 7:22 p.m.