Packages

class ViveknSentimentModel extends AnnotatorModel[ViveknSentimentModel] with HasSimpleAnnotate[ViveknSentimentModel] with ViveknSentimentUtils

Sentiment analyser inspired by the algorithm by Vivek Narayanan https://github.com/vivekn/sentiment/.

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

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

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.

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

See also

SentimentDetector for an alternative approach to sentiment detection

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

Instance Constructors

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

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 ViveknWordCount(er: ExternalResource, prune: Int, f: (List[String]) ⇒ Set[String], left: Map[String, Long] = ..., right: Map[String, Long] = ...): (Map[String, Long], Map[String, Long])
    Definition Classes
    ViveknSentimentUtils
  10. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  12. 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
    ViveknSentimentModel → HasSimpleAnnotate
  13. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  14. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. def classify(sentence: TokenizedSentence): (Short, Double)

    Positive: 0, Negative: 1, NA: 2

  17. final def clear(param: Param[_]): ViveknSentimentModel.this.type
    Definition Classes
    Params
  18. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  19. def copy(extra: ParamMap): ViveknSentimentModel

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  20. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  22. 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
  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 featureLimit: IntParam

    Content feature limit, to boost performance in very dirt text (Default: disabled with -1)

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

    Get Content feature limit, to boost performance in very dirt text (Default: disabled with -1)

  42. def getFeatures: Set[String]

    Set of unique words

  43. def getImportantFeatureRatio(v: Double): Double

    Get Proportion of feature content to be considered relevant (Default: 0.5)

  44. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  45. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  46. def getNegative: Map[String, Long]

    Count of negative words

  47. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  48. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  49. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  50. def getPositive: Map[String, Long]

    Count of positive words

  51. def getUnimportantFeatureStep(v: Double): Double

    Get Proportion to lookahead in unimportant features (Default: 0.025)

  52. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  53. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  54. def hasParent: Boolean
    Definition Classes
    Model
  55. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  56. val importantFeatureRatio: DoubleParam

    Proportion of feature content to be considered relevant (Default: 0.5)

  57. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  58. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : SENTIMENT

    Input annotator type : SENTIMENT

    Definition Classes
    ViveknSentimentModel → HasInputAnnotationCols
  60. 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
  61. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  62. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  63. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  64. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  65. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  66. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  67. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  74. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  79. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  80. def negateSequence(words: Array[String]): Set[String]

    Detects negations and transforms them into not_ form

    Detects negations and transforms them into not_ form

    Definition Classes
    ViveknSentimentUtils
  81. val negative: MapFeature[String, Long]

    negative_sentences

    negative_sentences

    Attributes
    protected
  82. val negativeTotals: LongParam

    Count of negative words

  83. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  84. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  85. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  86. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  87. val outputAnnotatorType: AnnotatorType

    Output annotator type : SENTIMENT

    Output annotator type : SENTIMENT

    Definition Classes
    ViveknSentimentModel → HasOutputAnnotatorType
  88. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  89. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  90. var parent: Estimator[ViveknSentimentModel]
    Definition Classes
    Model
  91. val positive: MapFeature[String, Long]

    positive_sentences

    positive_sentences

    Attributes
    protected
  92. val positiveTotals: LongParam

    Count of positive words

  93. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  94. def set[T](feature: StructFeature[T], value: T): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  96. def set[T](feature: SetFeature[T], value: Set[T]): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. def set[T](feature: ArrayFeature[T], value: Array[T]): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  98. final def set(paramPair: ParamPair[_]): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  99. final def set(param: String, value: Any): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  100. final def set[T](param: Param[T], value: T): ViveknSentimentModel.this.type
    Definition Classes
    Params
  101. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  105. final def setDefault(paramPairs: ParamPair[_]*): ViveknSentimentModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  106. final def setDefault[T](param: Param[T], value: T): ViveknSentimentModel.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  107. def setFeatureLimit(v: Int): ViveknSentimentModel.this.type

    Set Content feature limit, to boost performance in very dirt text (Default: disabled with -1)

  108. def setImportantFeatureRatio(v: Double): ViveknSentimentModel.this.type

    Set Proportion of feature content to be considered relevant (Default: 0.5)

  109. final def setInputCols(value: String*): ViveknSentimentModel.this.type
    Definition Classes
    HasInputAnnotationCols
  110. def setInputCols(value: Array[String]): ViveknSentimentModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  111. def setLazyAnnotator(value: Boolean): ViveknSentimentModel.this.type
    Definition Classes
    CanBeLazy
  112. final def setOutputCol(value: String): ViveknSentimentModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  113. def setParent(parent: Estimator[ViveknSentimentModel]): ViveknSentimentModel
    Definition Classes
    Model
  114. def setUnimportantFeatureStep(v: Double): ViveknSentimentModel.this.type

    Set Proportion to lookahead in unimportant features (Default: 0.025)

  115. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  116. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  117. 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
  118. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  119. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  120. 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
  121. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  122. val uid: String
    Definition Classes
    ViveknSentimentModel → Identifiable
  123. val unimportantFeatureStep: DoubleParam

    Proportion to lookahead in unimportant features (Default: 0.025)

  124. 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
  125. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  126. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  127. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  128. val words: SetFeature[String]

    words

    words

    Attributes
    protected
  129. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  130. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from ViveknSentimentUtils

Inherited from HasSimpleAnnotate[ViveknSentimentModel]

Inherited from AnnotatorModel[ViveknSentimentModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[ViveknSentimentModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[ViveknSentimentModel]

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

Parameter getters