Perform Natural Language Analysis

Analyse text entities, sentiment, syntax and categorisation 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")
)

Arguments

string

A vector of text to detect language for, or Google Cloud Storage URI(s)

nlp_type

The type of Natural Language Analysis to perform. The default annotateText will perform all features in one call.

type

Whether input text is plain text or a HTML page

language

Language of source, must be supported by API.

encodingType

Text encoding that the caller uses to process the output

Value

A list of the following objects, if those fields are asked for via nlp_type:

• sentences - Sentences in the input document

• tokens - Tokens, along with their syntactic information, in the input document

• entities - Entities, along with their semantic information, in the input document

• documentSentiment - The overall sentiment for the document

• classifyText -Classification of the document

• language - The language of the text, which will be the same as the language specified in the request or, if not specified, the automatically-detected language

• text - The original text passed into the API. NA if not passed due to being zero-length etc.

Details

string can be a character vector, or a location of a file content on Google cloud Storage. This URI must be of the form gs://bucket_name/object_name

Encoding type can usually be left at default UTF8. Read more here

The current language support is available here

See also

https://cloud.google.com/natural-language/docs/reference/rest/v1/documents

Examples

if (FALSE) { # \dontrun{

text <- "to administer medicince to animals is frequently a very difficult matter,
  and yet sometimes it's necessary to do so"
nlp <- gl_nlp(text)

nlp$sentences

nlp$tokens

nlp$entities

nlp$documentSentiment

## vectorised input
texts <- c("The cat sat one the mat", "oh no it didn't you fool")
nlp_results <- gl_nlp(texts)



} # }