sparknlp.annotator.ViveknSentimentApproach

class sparknlp.annotator.ViveknSentimentApproach[source]

Bases: sparknlp.common.AnnotatorApproach

Trains a sentiment analyser inspired by the algorithm by Vivek Narayanan.

The analyzer requires sentence boundaries to give a score in context. Tokenization is needed to make sure tokens are within bounds. Transitivity requirements are also required.

The training data needs to consist of a column for normalized text and a label column (either "positive" or "negative").

For extended examples of usage, see the Spark NLP Workshop.

Input Annotation types

Output Annotation type

TOKEN, DOCUMENT

SENTIMENT

Parameters
sentimentCol

column with the sentiment result of every row. Must be ‘positive’ or ‘negative’

pruneCorpus

Removes unfrequent scenarios from scope. The higher the better performance. Defaults 1

References

The algorithm is based on the paper “Fast and accurate sentiment classification using an enhanced Naive Bayes model”.

https://github.com/vivekn/sentiment/

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> document = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> token = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> normalizer = Normalizer() \
...     .setInputCols(["token"]) \
...     .setOutputCol("normal")
>>> vivekn = ViveknSentimentApproach() \
...     .setInputCols(["document", "normal"]) \
...     .setSentimentCol("train_sentiment") \
...     .setOutputCol("result_sentiment")
>>> finisher = Finisher() \
...     .setInputCols(["result_sentiment"]) \
...     .setOutputCols("final_sentiment")
>>> pipeline = Pipeline().setStages([document, token, normalizer, vivekn, finisher])
>>> training = spark.createDataFrame([
...     ("I really liked this movie!", "positive"),
...     ("The cast was horrible", "negative"),
...     ("Never going to watch this again or recommend it to anyone", "negative"),
...     ("It's a waste of time", "negative"),
...     ("I loved the protagonist", "positive"),
...     ("The music was really really good", "positive")
... ]).toDF("text", "train_sentiment")
>>> pipelineModel = pipeline.fit(training)
>>> data = spark.createDataFrame([
...     ["I recommend this movie"],
...     ["Dont waste your time!!!"]
... ]).toDF("text")
>>> result = pipelineModel.transform(data)
>>> result.select("final_sentiment").show(truncate=False)
+---------------+
|final_sentiment|
+---------------+
|[positive]     |
|[negative]     |
+---------------+

Methods

__init__()

clear(param)

Clears a param from the param map if it has been explicitly set.

copy([extra])

Creates a copy of this instance with the same uid and some extra params.

explainParam(param)

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap([extra])

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

fit(dataset[, params])

Fits a model to the input dataset with optional parameters.

fitMultiple(dataset, paramMaps)

Fits a model to the input dataset for each param map in paramMaps.

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value.

getOutputCol()

Gets output column name of annotations.

getParam(paramName)

Gets a param by its name.

getParamValue(paramName)

Gets the value of a parameter.

hasDefault(param)

Checks whether a param has a default value.

hasParam(paramName)

Tests whether this instance contains a param with a given (string) name.

isDefined(param)

Checks whether a param is explicitly set by user or has a default value.

isSet(param)

Checks whether a param is explicitly set by user.

load(path)

Reads an ML instance from the input path, a shortcut of read().load(path).

read()

Returns an MLReader instance for this class.

save(path)

Save this ML instance to the given path, a shortcut of 'write().save(path)'.

set(param, value)

Sets a parameter in the embedded param map.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

setPruneCorpus(value)

Sets the removal of unfrequent scenarios from scope, by default 1.

setSentimentCol(value)

Sets column with the sentiment result of every row.

write()

Returns an MLWriter instance for this ML instance.

Attributes

featureLimit

getter_attrs

importantFeatureRatio

inputCols

lazyAnnotator

outputCol

params

Returns all params ordered by name.

pruneCorpus

sentimentCol

unimportantFeatureStep

clear(param)

Clears a param from the param map if it has been explicitly set.

copy(extra=None)

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extra – Extra parameters to copy to the new instance

Returns

Copy of this instance

explainParam(param)

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters

extra – extra param values

Returns

merged param map

fit(dataset, params=None)

Fits a model to the input dataset with optional parameters.

Parameters
  • dataset – input dataset, which is an instance of pyspark.sql.DataFrame

  • params – an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

fitted model(s)

New in version 1.3.0.

fitMultiple(dataset, paramMaps)

Fits a model to the input dataset for each param map in paramMaps.

Parameters
  • dataset – input dataset, which is an instance of pyspark.sql.DataFrame.

  • paramMaps – A Sequence of param maps.

Returns

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

New in version 2.3.0.

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()

Gets output column name of annotations.

getParam(paramName)

Gets a param by its name.

getParamValue(paramName)

Gets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

hasDefault(param)

Checks whether a param has a default value.

hasParam(paramName)

Tests whether this instance contains a param with a given (string) name.

isDefined(param)

Checks whether a param is explicitly set by user or has a default value.

isSet(param)

Checks whether a param is explicitly set by user.

classmethod load(path)

Reads an ML instance from the input path, a shortcut of read().load(path).

property params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

classmethod read()

Returns an MLReader instance for this class.

save(path)

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)

Sets a parameter in the embedded param map.

setInputCols(*value)

Sets column names of input annotations.

Parameters
*valuestr

Input columns for the annotator

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters
valuebool

Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value)

Sets output column name of annotations.

Parameters
valuestr

Name of output column

setParamValue(paramName)

Sets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

setPruneCorpus(value)[source]

Sets the removal of unfrequent scenarios from scope, by default 1.

The higher the better performance.

Parameters
valueint

The frequency

setSentimentCol(value)[source]

Sets column with the sentiment result of every row.

Must be either ‘positive’ or ‘negative’.

Parameters
valuestr

Name of the column

uid

A unique id for the object.

write()

Returns an MLWriter instance for this ML instance.