sentence_similarity: Sentiment Analysis Scores

In transforEmotion: Sentiment Analysis for Text, Image and Video using Transformer Models

Sentiment Analysis Scores

Description

Uses sentiment analysis pipelines from huggingface to compute probabilities that the text corresponds to the specified classes

Usage

sentence_similarity(
  text,
  comparison_text,
  transformer = c("all_minilm_l6"),
  device = c("auto", "cpu", "cuda"),
  preprocess = FALSE,
  keep_in_env = TRUE,
  envir = 1
)

Arguments

text	Character vector or list. Text in a vector or list data format
comparison_text	Character vector or list. Text in a vector or list data format
transformer	Character. Specific sentence similarity transformer to be used. Defaults to "all_minilm_l6" (see huggingface) Also allows any sentence similarity models with a pipeline from huggingface to be used by using the specified name (e.g., "typeform/distilbert-base-uncased-mnli"; see Examples)
device	Character. Whether to use CPU or GPU for inference. Defaults to "auto" which will use GPU over CPU (if CUDA-capable GPU is setup). Set to "cpu" to perform over CPU
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 FALSE. Transformers generally are OK without preprocessing and handle many of these functions internally, so setting to TRUE will not change performance much
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

Value

Returns a n x m similarity matrix where n is length of text and m is the length of comparison_text

Author(s)

Alexander P. Christensen <[e-mail]>

Examples

# Load data
data(neo_ipip_extraversion)

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

## Not run: 
# Example with defaults
sentence_similarity(
 text = text, comparison_text = text
)

# Example with model from 'sentence-transformers'
sentence_similarity(
 text = text, comparison_text = text,
 transformer = "sentence-transformers/all-mpnet-base-v2"
)


## End(Not run)

transforEmotion documentation built on Sept. 11, 2024, 9:26 p.m.