Class/Object

com.johnsnowlabs.nlp.annotators.sda.vivekn

ViveknSentimentModel

Related Docs: object ViveknSentimentModel | package vivekn

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

Inspired on vivekn sentiment analysis algorithm https://github.com/vivekn/sentiment/.

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

See https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/annotators/sda/vivekn for further reference on how to use this API.

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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()

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  2. new ViveknSentimentModel(uid: String)

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Type Members

  1. type AnnotationContent = Seq[Row]

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    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

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    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T

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    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean

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    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])

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    Definition Classes
    ViveknSentimentUtils
  10. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def afterAnnotate(dataset: DataFrame): DataFrame

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  12. def annotate(annotations: Seq[Annotation]): Seq[Annotation]

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    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
    ViveknSentimentModelHasSimpleAnnotate
  13. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  14. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. def classify(sentence: TokenizedSentence): (Short, Double)

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    Positive: 0, Negative: 1, NA: 2

  17. final def clear(param: Param[_]): ViveknSentimentModel.this.type

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    Definition Classes
    Params
  18. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  19. def copy(extra: ParamMap): ViveknSentimentModel

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    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

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    Attributes
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    Params
  21. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  22. def dfAnnotate: UserDefinedFunction

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    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

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    Definition Classes
    AnyRef
  24. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  25. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  26. def explainParams(): String

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    Definition Classes
    Params
  27. def extraValidate(structType: StructType): Boolean

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  28. def extraValidateMsg: String

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    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  29. final def extractParamMap(): ParamMap

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    Definition Classes
    Params
  30. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params
  31. val featureLimit: IntParam

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    content feature limit, to boost performance in very dirt text.

    content feature limit, to boost performance in very dirt text. Default disabled with -1

    Attributes
    protected
  32. val features: ArrayBuffer[Feature[_, _, _]]

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    Definition Classes
    HasFeatures
  33. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  34. def get[T](feature: StructFeature[T]): Option[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  35. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  36. def get[T](feature: SetFeature[T]): Option[Set[T]]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  37. def get[T](feature: ArrayFeature[T]): Option[Array[T]]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  38. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  39. final def getClass(): Class[_]

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    Definition Classes
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  40. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  41. def getFeatureLimit(v: Int): Int

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    Get content feature limit, to boost performance in very dirt text.

    Get content feature limit, to boost performance in very dirt text. Default disabled with -1

  42. def getFeatures: Set[String]

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    Set of unique words

  43. def getImportantFeatureRatio(v: Double): Double

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    Get Proportion of feature content to be considered relevant.

    Get Proportion of feature content to be considered relevant. Defaults to 0.5

  44. def getInputCols: Array[String]

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    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  45. def getLazyAnnotator: Boolean

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    Definition Classes
    CanBeLazy
  46. def getNegative: Map[String, Long]

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    Count of negative words

  47. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  48. final def getOutputCol: String

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    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  49. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  50. def getPositive: Map[String, Long]

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    Count of positive words

  51. def getUnimportantFeatureStep(v: Double): Double

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    Get Proportion to lookahead in unimportant features.

    Get Proportion to lookahead in unimportant features. Defaults to 0.025

  52. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params
  53. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params
  54. def hasParent: Boolean

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    Definition Classes
    Model
  55. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  56. val importantFeatureRatio: DoubleParam

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    proportion of feature content to be considered relevant.

    proportion of feature content to be considered relevant. Defaults to 0.5

    Attributes
    protected
  57. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  58. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  59. val inputAnnotatorTypes: Array[AnnotatorType]

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    Input annotator type : SENTIMENT

    Input annotator type : SENTIMENT

    Definition Classes
    ViveknSentimentModelHasInputAnnotationCols
  60. final val inputCols: StringArrayParam

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    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

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    Params
  62. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  63. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  64. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  65. val lazyAnnotator: BooleanParam

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    Definition Classes
    CanBeLazy
  66. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  67. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  68. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  69. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  70. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  71. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  72. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  73. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  74. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  75. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  76. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  77. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  78. def msgHelper(schema: StructType): String

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  79. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  80. def negateSequence(words: Array[String]): Set[String]

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    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]

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    negative_sentences

    negative_sentences

    Attributes
    protected
  82. val negativeTotals: LongParam

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    count of negative words

    count of negative words

    Attributes
    protected
  83. final def notify(): Unit

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    Definition Classes
    AnyRef
  84. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  85. def onWrite(path: String, spark: SparkSession): Unit

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    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  86. val outputAnnotatorType: AnnotatorType

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    Output annotator type : SENTIMENT

    Output annotator type : SENTIMENT

    Definition Classes
    ViveknSentimentModelHasOutputAnnotatorType
  87. final val outputCol: Param[String]

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    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  88. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  89. var parent: Estimator[ViveknSentimentModel]

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    Definition Classes
    Model
  90. val positive: MapFeature[String, Long]

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    positive_sentences

    positive_sentences

    Attributes
    protected
  91. val positiveTotals: LongParam

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    count of positive words

    count of positive words

    Attributes
    protected
  92. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  93. def set[T](feature: StructFeature[T], value: T): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  95. def set[T](feature: SetFeature[T], value: Set[T]): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  96. def set[T](feature: ArrayFeature[T], value: Array[T]): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  97. final def set(paramPair: ParamPair[_]): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    Params
  98. final def set(param: String, value: Any): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    Params
  99. final def set[T](param: Param[T], value: T): ViveknSentimentModel.this.type

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    Definition Classes
    Params
  100. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  101. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  104. final def setDefault(paramPairs: ParamPair[_]*): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    Params
  105. final def setDefault[T](param: Param[T], value: T): ViveknSentimentModel.this.type

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    Attributes
    protected
    Definition Classes
    Params
  106. def setFeatureLimit(v: Int): ViveknSentimentModel.this.type

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    Set content feature limit, to boost performance in very dirt text.

    Set content feature limit, to boost performance in very dirt text. Default disabled with -1

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

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    Set Proportion of feature content to be considered relevant.

    Set Proportion of feature content to be considered relevant. Defaults to 0.5

  108. final def setInputCols(value: String*): ViveknSentimentModel.this.type

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

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    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  110. def setLazyAnnotator(value: Boolean): ViveknSentimentModel.this.type

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

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    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  112. def setParent(parent: Estimator[ViveknSentimentModel]): ViveknSentimentModel

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    Definition Classes
    Model
  113. def setUnimportantFeatureStep(v: Double): ViveknSentimentModel.this.type

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    Set Proportion to lookahead in unimportant features.

    Set Proportion to lookahead in unimportant features. Defaults to 0.025

  114. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  115. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  116. final def transform(dataset: Dataset[_]): DataFrame

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    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
  117. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  118. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  119. final def transformSchema(schema: StructType): StructType

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    requirement for pipeline transformation validation.

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

    Definition Classes
    RawAnnotator → PipelineStage
  120. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  121. val uid: String

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    Definition Classes
    ViveknSentimentModel → Identifiable
  122. val unimportantFeatureStep: DoubleParam

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    proportion to lookahead in unimportant features.

    proportion to lookahead in unimportant features. Defaults to 0.025

    Attributes
    protected
  123. def validate(schema: StructType): Boolean

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    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
  124. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  125. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  126. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  127. val words: SetFeature[String]

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    words

    words

    Attributes
    protected
  128. def wrapColumnMetadata(col: Column): Column

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  129. def write: MLWriter

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    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from ViveknSentimentUtils

Inherited from CanBeLazy

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

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters