Packages

class SentimentDetectorModel extends AnnotatorModel[SentimentDetectorModel] with HasSimpleAnnotate[SentimentDetectorModel]

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

This is the instantiated model of the SentimentDetector. For training your own model, please see the documentation of that class.

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 either in the form of a delimited text file or directly as an ExternalResource.

By default, the sentiment score will be assigned labels "positive" if the score is >= 0, else "negative". To retrieve the raw sentiment scores, enableScore needs to be set to true.

For extended examples of usage, see the Examples and the SentimentTestSpec.

See also

ViveknSentimentApproach for an alternative approach to sentiment extraction

Linear Supertypes
HasSimpleAnnotate[SentimentDetectorModel], AnnotatorModel[SentimentDetectorModel], CanBeLazy, RawAnnotator[SentimentDetectorModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[SentimentDetectorModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. SentimentDetectorModel
  2. HasSimpleAnnotate
  3. AnnotatorModel
  4. CanBeLazy
  5. RawAnnotator
  6. HasOutputAnnotationCol
  7. HasInputAnnotationCols
  8. HasOutputAnnotatorType
  9. ParamsAndFeaturesWritable
  10. HasFeatures
  11. DefaultParamsWritable
  12. MLWritable
  13. Model
  14. Transformer
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new SentimentDetectorModel()
  2. new SentimentDetectorModel(uid: String)

    uid

    internal uid needed for saving annotator to disk

Type Members

  1. type AnnotationContent = Seq[Row]

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def annotate(annotations: Seq[Annotation]): Seq[Annotation]

    Tokens are needed to identify each word in a sentence boundary POS tags are optionally submitted to the model in case they are needed Lemmas are another optional annotator for some models Bounds of sentiment are hardcoded to 0 as they render useless

    Tokens are needed to identify each word in a sentence boundary POS tags are optionally submitted to the model in case they are needed Lemmas are another optional annotator for some models Bounds of sentiment are hardcoded to 0 as they render useless

    annotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    SentimentDetectorModel → HasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  14. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  15. final def clear(param: Param[_]): SentimentDetectorModel.this.type
    Definition Classes
    Params
  16. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  17. def copy(extra: ParamMap): SentimentDetectorModel

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  18. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  19. val decrementMultiplier: DoubleParam

    Multiplier for decrement sentiments (Default: -2.0)

  20. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. def dfAnnotate: UserDefinedFunction

    Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column

    Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column

    returns

    udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation

    Definition Classes
    HasSimpleAnnotate
  22. val enableScore: BooleanParam

    if true, score will show as a string type containing a double value, else will output string "positive" or "negative" (Default: false)

  23. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  25. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  26. def explainParams(): String
    Definition Classes
    Params
  27. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  28. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  29. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  30. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  31. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  32. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  33. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  34. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  35. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  36. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  37. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  38. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  39. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  40. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  41. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  42. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  43. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  44. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  45. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  46. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  47. def hasParent: Boolean
    Definition Classes
    Model
  48. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  49. val incrementMultiplier: DoubleParam

    Multiplier for increment sentiments (Default: 2.0)

  50. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotation type : TOKEN, DOCUMENT

    Input annotation type : TOKEN, DOCUMENT

    Definition Classes
    SentimentDetectorModel → HasInputAnnotationCols
  53. final val inputCols: StringArrayParam

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  54. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  55. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  56. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  57. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  58. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  59. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. lazy val model: PragmaticScorer

  72. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  73. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  74. val negativeMultiplier: DoubleParam

    Multiplier for negative sentiments (Default: -1.0)

  75. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  76. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  77. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  78. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  79. val outputAnnotatorType: AnnotatorType

    Output annotation type : SENTIMENT

    Output annotation type : SENTIMENT

    Definition Classes
    SentimentDetectorModel → HasOutputAnnotatorType
  80. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  81. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  82. var parent: Estimator[SentimentDetectorModel]
    Definition Classes
    Model
  83. val positiveMultiplier: DoubleParam

    Multiplier for positive sentiments (Default: 1.0)

  84. val reverseMultiplier: DoubleParam

    Multiplier for revert sentiments (Default: -1.0)

  85. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  86. val sentimentDict: MapFeature[String, String]

    Sentiment dict

  87. def set[T](feature: StructFeature[T], value: T): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  88. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  89. def set[T](feature: SetFeature[T], value: Set[T]): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  90. def set[T](feature: ArrayFeature[T], value: Array[T]): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  91. final def set(paramPair: ParamPair[_]): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  92. final def set(param: String, value: Any): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  93. final def set[T](param: Param[T], value: T): SentimentDetectorModel.this.type
    Definition Classes
    Params
  94. def setDecrementMultipler(v: Double): SentimentDetectorModel.this.type

    Multiplier for decrement sentiments (Default: -2.0)

  95. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  96. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  98. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  99. final def setDefault(paramPairs: ParamPair[_]*): SentimentDetectorModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  100. final def setDefault[T](param: Param[T], value: T): SentimentDetectorModel.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  101. def setEnableScore(v: Boolean): SentimentDetectorModel.this.type

    If true, score will show as a string type containing a double value, else will output string "positive" or "negative" (Default: false)

  102. def setIncrementMultipler(v: Double): SentimentDetectorModel.this.type

    Multiplier for increment sentiments (Default: 2.0)

  103. final def setInputCols(value: String*): SentimentDetectorModel.this.type
    Definition Classes
    HasInputAnnotationCols
  104. def setInputCols(value: Array[String]): SentimentDetectorModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  105. def setLazyAnnotator(value: Boolean): SentimentDetectorModel.this.type
    Definition Classes
    CanBeLazy
  106. def setNegativeMultipler(v: Double): SentimentDetectorModel.this.type

    Multiplier for negative sentiments (Default: -1.0)

  107. final def setOutputCol(value: String): SentimentDetectorModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  108. def setParent(parent: Estimator[SentimentDetectorModel]): SentimentDetectorModel
    Definition Classes
    Model
  109. def setPositiveMultipler(v: Double): SentimentDetectorModel.this.type

    Multiplier for positive sentiments (Default: 1.0)

  110. def setReverseMultipler(v: Double): SentimentDetectorModel.this.type

    Multiplier for revert sentiments (Default: -1.0)

  111. def setSentimentDict(value: Map[String, String]): SentimentDetectorModel.this.type

    Path to file with list of inputs and their content, with such delimiter, readAs LINE_BY_LINE or as SPARK_DATASET.

    Path to file with list of inputs and their content, with such delimiter, readAs LINE_BY_LINE or as SPARK_DATASET. If latter is set, options is passed to spark reader.

  112. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  113. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  114. final def transform(dataset: Dataset[_]): DataFrame

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    dataset

    Dataset[Row]

    Definition Classes
    AnnotatorModel → Transformer
  115. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  116. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  117. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    RawAnnotator → PipelineStage
  118. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  119. val uid: String
    Definition Classes
    SentimentDetectorModel → Identifiable
  120. def validate(schema: StructType): Boolean

    takes a Dataset and checks to see if all the required annotation types are present.

    takes a Dataset and checks to see if all the required annotation types are present.

    schema

    to be validated

    returns

    True if all the required types are present, else false

    Attributes
    protected
    Definition Classes
    RawAnnotator
  121. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  122. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  123. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  124. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  125. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from HasSimpleAnnotate[SentimentDetectorModel]

Inherited from AnnotatorModel[SentimentDetectorModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[SentimentDetectorModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[SentimentDetectorModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.

Annotator types

Required input and expected output annotator types

Members

Parameter setters