| textaKeyPhrases {mscstexta4r} | R Documentation |
Returns the key talking points in sentences or documents.
Description
This function returns the the key talking points in a list of sentences or documents. The following languages are currently supported: English, German, Spanish and Japanese.
Internally, this function invokes the Microsoft Cognitive Services Text Analytics REST API documented at https://www.microsoft.com/cognitive-services/en-us/text-analytics/documentation.
You MUST have a valid Microsoft Cognitive Services account and an API key for this function to work properly. See https://www.microsoft.com/cognitive-services/en-us/pricing for details.
Usage
textaKeyPhrases(documents, languages = rep("en", length(documents)))
Arguments
documents |
(character vector) Vector of sentences or documents for which to extract key talking points. |
languages |
(character vector) Languages of the sentences or documents, supported values: "en"(English, default), "de"(German), "es"(Spanish), "fr"(French), "ja"(Japanese) |
Value
An S3 object of the class texta. The results are stored
in the results dataframe inside this object. The dataframe contains
the original sentences or documents and their key talking points. If an error
occurred during processing, the dataframe will also have an error
column that describes the error.
Author(s)
Phil Ferriere pferriere@hotmail.com
Examples
## Not run:
docsText <- c(
"Loved the food, service and atmosphere! We'll definitely be back.",
"Very good food, reasonable prices, excellent service.",
"It was a great restaurant.",
"If steak is what you want, this is the place.",
"The atmosphere is pretty bad but the food is quite good.",
"The food is quite good but the atmosphere is pretty bad.",
"I'm not sure I would come back to this restaurant.",
"The food wasn't very good.",
"While the food was good the service was a disappointment.",
"I was very disappointed with both the service and my entree."
)
docsLanguage <- rep("en", length(docsText))
tryCatch({
# Get key talking points in documents
docsKeyPhrases <- textaKeyPhrases(
documents = docsText, # Input sentences or documents
languages = docsLanguage
# "en"(English, default)|"de"(German)|"es"(Spanish)|"fr"(French)|"ja"(Japanese)
)
# Class and structure of docsKeyPhrases
class(docsKeyPhrases)
#> [1] "texta"
str(docsKeyPhrases, max.level = 1)
#> List of 3
#> $ results:'data.frame': 10 obs. of 2 variables:
#> $ json : chr "{\"documents\":[{\"keyPhrases\":[\"atmosphere\",\"food\", __truncated__ ]}]}
#> $ request:List of 7
#> ..- attr(*, "class")= chr "request"
#> - attr(*, "class")= chr "texta"
# Print results
docsKeyPhrases
#> texta [https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/keyPhrases]
#>
#> -----------------------------------------------------------
#> text keyPhrases
#> ------------------------------ ----------------------------
#> Loved the food, service and atmosphere, food, service
#> atmosphere! We'll definitely
#> be back.
#>
#> Very good food, reasonable reasonable prices, good food
#> prices, excellent service.
#>
#> It was a great restaurant. great restaurant
#>
#> If steak is what you want, steak, place
#> this is the place.
#>
#> The atmosphere is pretty bad atmosphere, food
#> but the food is quite good.
#>
#> The food is quite good but the food, atmosphere
#> atmosphere is pretty bad.
#>
#> I'm not sure I would come back restaurant
#> to this restaurant.
#>
#> The food wasn't very good. food
#>
#> While the food was good the service, food
#> service was a disappointment.
#>
#> I was very disappointed with service, entree
#> both the service and my
#> entree.
#> -----------------------------------------------------------
}, error = function(err) {
# Print error
geterrmessage()
})
## End(Not run)