nlp_vivekn_sentiment_detector: Spark NLP ViveknSentimentApproach

In r-spark/sparknlp: R Interface to John Snow Labs Spark NLP

Spark NLP ViveknSentimentApproach

Description

Spark ML estimator that scores a sentence for a sentiment See https://nlp.johnsnowlabs.com/docs/en/annotators#viveknsentimentdetector

Usage

nlp_vivekn_sentiment_detector(
  x,
  input_cols,
  output_col,
  sentiment_col,
  prune_corpus = NULL,
  feature_limit = NULL,
  unimportant_feature_step = NULL,
  important_feature_ratio = NULL,
  uid = random_string("vivekn_sentiment_detector_")
)

Arguments

x	A spark_connection, ml_pipeline, or a tbl_spark.
input_cols	Input columns. String array.
output_col	Output column. String.
sentiment_col	Column with sentiment analysis row’s result for training.
prune_corpus	when training on small data you may want to disable this to not cut off infrequent words
feature_limit	Set content feature limit, to boost performance in very dirt text.
unimportant_feature_step	Set Proportion to lookahead in unimportant features.
important_feature_ratio	Set Proportion of feature content to be considered relevant.
uid	A character string used to uniquely identify the ML estimator.

Value

The object returned depends on the class of x.

• spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to a Spark Estimator object and can be used to compose Pipeline objects.

• ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the NLP estimator appended to the pipeline.

• tbl_spark: When x is a tbl_spark, an estimator is constructed then immediately fit with the input tbl_spark, returning an NLP model.

r-spark/sparknlp documentation built on Oct. 15, 2022, 10:50 a.m.