sparknlp.annotator.SentimentDetector

class sparknlp.annotator.SentimentDetector[source]

Bases: sparknlp.common.AnnotatorApproach

Trains a rule based sentiment detector, which calculates a score based on predefined keywords.

A dictionary of predefined sentiment keywords must be provided with setDictionary(), where each line is a word delimited to its class (either positive or negative). The dictionary can be set in the form of a delimited text file.

By default, the sentiment score will be assigned labels "positive" if the score is >= 0, else "negative".

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

Input Annotation types

Output Annotation type

TOKEN, DOCUMENT

SENTIMENT

Parameters
dictionary

path for dictionary to sentiment analysis

Examples

In this example, the dictionary default-sentiment-dict.txt has the form of:

...
cool,positive
superb,positive
bad,negative
uninspired,negative
...

where each sentiment keyword is delimited by ",".

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> lemmatizer = Lemmatizer() \
...     .setInputCols(["token"]) \
...     .setOutputCol("lemma") \
...     .setDictionary("lemmas_small.txt", "->", "\t")
>>> sentimentDetector = SentimentDetector() \
...     .setInputCols(["lemma", "document"]) \
...     .setOutputCol("sentimentScore") \
...     .setDictionary("default-sentiment-dict.txt", ",", ReadAs.TEXT)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     tokenizer,
...     lemmatizer,
...     sentimentDetector,
... ])
>>> data = spark.createDataFrame([
...     ["The staff of the restaurant is nice"],
...     ["I recommend others to avoid because it is too expensive"]
... ]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("sentimentScore.result").show(truncate=False)
+----------+
|result    |
+----------+
|[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.

setDictionary(path, delimiter[, read_as, ...])

Sets path for dictionary to sentiment analysis

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.

write()

Returns an MLWriter instance for this ML instance.

Attributes

decrementMultiplier

dictionary

enableScore

getter_attrs

incrementMultiplier

inputCols

lazyAnnotator

negativeMultiplier

outputCol

params

Returns all params ordered by name.

positiveMultiplier

reverseMultiplier

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.

setDictionary(path, delimiter, read_as='TEXT', options={'format': 'text'})[source]

Sets path for dictionary to sentiment analysis

Parameters
pathstr

Path to dictionary file

delimiterstr

Delimiter for entries

read_assttr, optional

How to read the resource, by default ReadAs.TEXT

optionsdict, optional

Options for reading the resource, by default {‘format’: ‘text’}

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

uid

A unique id for the object.

write()

Returns an MLWriter instance for this ML instance.