gl_nlp: Perform Natural Language Analysis

Description

Analyse text for entities, sentiment, syntax and classification using the Google Natural Language API.

Usage

gl_nlp(
  string,
  nlp_type = c("annotateText", "analyzeEntities", "analyzeSentiment", "analyzeSyntax",
    "analyzeEntitySentiment", "classifyText"),
  type = c("PLAIN_TEXT", "HTML"),
  language = c("en", "zh", "zh-Hant", "fr", "de", "it", "ja", "ko", "pt", "es"),
  encodingType = c("UTF8", "UTF16", "UTF32", "NONE")
)

Value

A list containing the requested components as specified by nlp_type:

sentences: Sentences in the input document. API reference.
tokens: Tokens with syntactic information. API reference.
entities: Entities with semantic information. API reference.
documentSentiment: Overall sentiment of the document. API reference.
classifyText: Document classification. API reference.
language: Detected language of the text, or the language specified in the request.
text: Original text passed to the API. Returns NA if input is empty.

Arguments

string: Character vector. Text to analyse or Google Cloud Storage URI(s) in the form gs://bucket_name/object_name.
nlp_type: Character. Type of analysis to perform. Default annotateText performs all features in a single call. Options include: analyzeEntities, analyzeSentiment, analyzeSyntax, analyzeEntitySentiment, classifyText.
type: Character. Whether the input is plain text (PLAIN_TEXT) or HTML (HTML).
language: Character. Language of the source text. Must be supported by the API.
encodingType: Character. Text encoding used to process the output. Default UTF8.

Details

Encoding type can usually be left at the default UTF8. Further details on encoding types.

Current language support is listed here.

Examples

Run this code

if (FALSE) {
library(googleLanguageR)

text <- "To administer medicine to animals is frequently difficult, yet sometimes necessary."
nlp <- gl_nlp(text)

nlp$sentences
nlp$tokens
nlp$entities
nlp$documentSentiment

# Vectorised input
texts <- c("The cat sat on the mat.", "Oh no, it did not, you fool!")
nlp_results <- gl_nlp(texts)
}