| title | getSentiment function (MicrosoftML) | ||||||
|---|---|---|---|---|---|---|---|
| description | Scores natural language text and creates a column that contains probabilities that the sentiments in the text are positive. | ||||||
| author | rothja | ||||||
| ms.author | jroth | ||||||
| ms.date | 07/15/2019 | ||||||
| ms.service | sql | ||||||
| ms.subservice | machine-learning | ||||||
| ms.topic | reference | ||||||
| keywords |
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| monikerRange | >=sql-server-2016||>=sql-server-linux-ver15 |
Scores natural language text and creates a column that contains probabilities that the sentiments in the text are positive.
getSentiment(vars, ...)
A character vector or list of variable names to transform. If named, the names represent the names of new variables to be created.
Additional arguments sent to compute engine.
The getSentiment transform returns the probability
that the sentiment of a natural text is positive. Currently supports
only the English language.
A maml object defining the transform.
Microsoft Corporation Microsoft Technical Support
rxFastTrees, rxFastForest, rxNeuralNet, rxOneClassSvm, rxLogisticRegression, rxFastLinear.
# Create the data
CustomerReviews <- data.frame(Review = c(
"I really did not like the taste of it",
"It was surprisingly quite good!",
"I will never ever ever go to that place again!!"),
stringsAsFactors = FALSE)
# Get the sentiment scores
sentimentScores <- rxFeaturize(data = CustomerReviews,
mlTransforms = getSentiment(vars = list(SentimentScore = "Review")))
# Let's translate the score to something more meaningful
sentimentScores$PredictedRating <- ifelse(sentimentScores$SentimentScore > 0.6,
"AWESOMENESS", "BLAH")
# Let's look at the results
sentimentScores