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com.johnsnowlabs.nlp.annotators.sda.vivekn

ViveknSentimentApproach

class ViveknSentimentApproach extends AnnotatorApproach[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.

Linear Supertypes
ViveknSentimentUtils, AnnotatorApproach[ViveknSentimentModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[ViveknSentimentModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. ViveknSentimentApproach
  2. ViveknSentimentUtils
  3. AnnotatorApproach
  4. CanBeLazy
  5. DefaultParamsWritable
  6. MLWritable
  7. HasOutputAnnotatorType
  8. HasOutputAnnotationCol
  9. HasInputAnnotationCols
  10. Estimator
  11. PipelineStage
  12. Logging
  13. Params
  14. Serializable
  15. Serializable
  16. Identifiable
  17. AnyRef
  18. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

Type Members

  1. 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. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. 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
  6. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): ViveknSentimentModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  9. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  10. final def clear(param: Param[_]): ViveknSentimentApproach.this.type
    Definition Classes
    Params
  11. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  12. final def copy(extra: ParamMap): Estimator[ViveknSentimentModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  13. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  14. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  15. val description: String

    Vivekn inspired sentiment analysis model

    Vivekn inspired sentiment analysis model

    Definition Classes
    ViveknSentimentApproachAnnotatorApproach
  16. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  18. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  19. def explainParams(): String
    Definition Classes
    Params
  20. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  22. val featureLimit: IntParam

    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
  23. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. final def fit(dataset: Dataset[_]): ViveknSentimentModel
    Definition Classes
    AnnotatorApproach → Estimator
  25. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[ViveknSentimentModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  26. def fit(dataset: Dataset[_], paramMap: ParamMap): ViveknSentimentModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  27. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): ViveknSentimentModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  28. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  29. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  30. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  31. def getFeatureLimit(v: Int): Int

    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

  32. def getImportantFeatureRatio(v: Double): Double

    Get Proportion of feature content to be considered relevant.

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

  33. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  34. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  35. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  36. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  37. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  38. def getUnimportantFeatureStep(v: Double): Double

    Get Proportion to lookahead in unimportant features.

    Get Proportion to lookahead in unimportant features. Defaults to 0.025

  39. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  40. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  41. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  42. val importantFeatureRatio: DoubleParam

    proportion of feature content to be considered relevant.

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

    Attributes
    protected
  43. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  44. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  45. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : TOKEN, DOCUMENT

    Input annotator type : TOKEN, DOCUMENT

    Definition Classes
    ViveknSentimentApproachHasInputAnnotationCols
  46. 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
  47. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  48. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  49. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  50. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  52. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  53. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  60. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  65. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  66. 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
  67. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  68. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  69. def onTrained(model: ViveknSentimentModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  70. val outputAnnotatorType: AnnotatorType

    Output annotator type : SENTIMENT

    Output annotator type : SENTIMENT

    Definition Classes
    ViveknSentimentApproachHasOutputAnnotatorType
  71. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  72. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  73. val pruneCorpus: IntParam

    Removes unfrequent scenarios from scope.

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

  74. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  75. val sentimentCol: Param[String]

    column with the sentiment result of every row.

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

  76. final def set(paramPair: ParamPair[_]): ViveknSentimentApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  77. final def set(param: String, value: Any): ViveknSentimentApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  78. final def set[T](param: Param[T], value: T): ViveknSentimentApproach.this.type
    Definition Classes
    Params
  79. def setCorpusPrune(value: Int): ViveknSentimentApproach.this.type

    when training on small data you may want to disable this to not cut off infrequent words

  80. final def setDefault(paramPairs: ParamPair[_]*): ViveknSentimentApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  81. final def setDefault[T](param: Param[T], value: T): ViveknSentimentApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  82. def setFeatureLimit(v: Int): ViveknSentimentApproach.this.type

    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

  83. def setImportantFeatureRatio(v: Double): ViveknSentimentApproach.this.type

    Set Proportion of feature content to be considered relevant.

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

  84. final def setInputCols(value: String*): ViveknSentimentApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  85. final def setInputCols(value: Array[String]): ViveknSentimentApproach.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  86. def setLazyAnnotator(value: Boolean): ViveknSentimentApproach.this.type
    Definition Classes
    CanBeLazy
  87. final def setOutputCol(value: String): ViveknSentimentApproach.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  88. def setSentimentCol(value: String): ViveknSentimentApproach.this.type

    Column with sentiment analysis row’s result for training.

    Column with sentiment analysis row’s result for training. If not set, external sources need to be set instead. Column with the sentiment result of every row. Must be 'positive' or 'negative'

  89. def setUnimportantFeatureStep(v: Double): ViveknSentimentApproach.this.type

    Set Proportion to lookahead in unimportant features.

    Set Proportion to lookahead in unimportant features. Defaults to 0.025

  90. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  91. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  92. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): ViveknSentimentModel
  93. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

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

    Definition Classes
    AnnotatorApproach → PipelineStage
  94. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  95. val uid: String
    Definition Classes
    ViveknSentimentApproach → Identifiable
  96. val unimportantFeatureStep: DoubleParam

    proportion to lookahead in unimportant features.

    proportion to lookahead in unimportant features. Defaults to 0.025

    Attributes
    protected
  97. 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
    AnnotatorApproach
  98. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  99. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  100. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  101. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from ViveknSentimentUtils

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[ViveknSentimentModel]

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