Packages

class SentimentDetector extends AnnotatorApproach[SentimentDetectorModel]

Gives a good or bad score to a sentence based on the approach used

Linear Supertypes
AnnotatorApproach[SentimentDetectorModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[SentimentDetectorModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. SentimentDetector
  2. AnnotatorApproach
  3. CanBeLazy
  4. DefaultParamsWritable
  5. MLWritable
  6. HasOutputAnnotatorType
  7. HasOutputAnnotationCol
  8. HasInputAnnotationCols
  9. Estimator
  10. PipelineStage
  11. Logging
  12. Params
  13. Serializable
  14. Serializable
  15. Identifiable
  16. AnyRef
  17. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

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

    uid

    internal uid needed for saving annotator to disk

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

    multiplier for decrement sentiments.

    multiplier for decrement sentiments. Defaults -2.0

  14. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  15. val description: String

    Rule based sentiment detector

    Rule based sentiment detector

    Definition Classes
    SentimentDetectorAnnotatorApproach
  16. val dictionary: ExternalResourceParam

    delimited file with a list sentiment tags per word.

    delimited file with a list sentiment tags per word. Requires 'delimiter' in options

  17. val enableScore: BooleanParam

    if true, score will show as the double value, else will output string \"positive\" or \"negative\".

    if true, score will show as the double value, else will output string \"positive\" or \"negative\". Defaults false

  18. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  19. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  20. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  21. def explainParams(): String
    Definition Classes
    Params
  22. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  23. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  24. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. final def fit(dataset: Dataset[_]): SentimentDetectorModel
    Definition Classes
    AnnotatorApproach → Estimator
  26. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[SentimentDetectorModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  27. def fit(dataset: Dataset[_], paramMap: ParamMap): SentimentDetectorModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  28. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): SentimentDetectorModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  29. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  30. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  31. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  32. def getInputCols: Array[String]

    returns

    input annotations columns currently used

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  36. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  37. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  38. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  39. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  40. val incrementMultiplier: DoubleParam

    multiplier for increment sentiments.

    multiplier for increment sentiments. Defaults 2.0

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

    Input annotation type : TOKEN, DOCUMENT

    Input annotation type : TOKEN, DOCUMENT

    Definition Classes
    SentimentDetectorHasInputAnnotationCols
  44. 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
  45. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  46. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  47. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  48. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  49. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  50. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  51. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  58. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  63. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  64. val negativeMultiplier: DoubleParam

    "multiplier for negative sentiments.

    "multiplier for negative sentiments. Defaults -1.0

  65. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  66. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  67. def onTrained(model: SentimentDetectorModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  68. val outputAnnotatorType: AnnotatorType

    Output annotation type : SENTIMENT

    Output annotation type : SENTIMENT

    Definition Classes
    SentimentDetectorHasOutputAnnotatorType
  69. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  70. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  71. val positiveMultiplier: DoubleParam

    multiplier for positive sentiments.

    multiplier for positive sentiments. Defaults 1.0

  72. val reverseMultiplier: DoubleParam

    multiplier for revert sentiments.

    multiplier for revert sentiments. Defaults -1.0

  73. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  74. final def set(paramPair: ParamPair[_]): SentimentDetector.this.type
    Attributes
    protected
    Definition Classes
    Params
  75. final def set(param: String, value: Any): SentimentDetector.this.type
    Attributes
    protected
    Definition Classes
    Params
  76. final def set[T](param: Param[T], value: T): SentimentDetector.this.type
    Definition Classes
    Params
  77. def setDecrementMultiplier(v: Double): SentimentDetector.this.type

    Multiplier for decrement sentiments.

    Multiplier for decrement sentiments. Defaults -2.0

  78. final def setDefault(paramPairs: ParamPair[_]*): SentimentDetector.this.type
    Attributes
    protected
    Definition Classes
    Params
  79. final def setDefault[T](param: Param[T], value: T): SentimentDetector.this.type
    Attributes
    protected
    Definition Classes
    Params
  80. def setDictionary(path: String, delimiter: String, readAs: Format, options: Map[String, String] = Map("format" -> "text")): SentimentDetector.this.type

    delimited file with a list sentiment tags per word.

    delimited file with a list sentiment tags per word. Requires 'delimiter' in options. Dictionary needs 'delimiter' in order to separate words from sentiment tags

  81. def setDictionary(value: ExternalResource): SentimentDetector.this.type

    delimited file with a list sentiment tags per word.

    delimited file with a list sentiment tags per word. Requires 'delimiter' in options. Dictionary needs 'delimiter' in order to separate words from sentiment tags

  82. def setEnableScore(v: Boolean): SentimentDetector.this.type

    if true, score will show as the double value, else will output string \"positive\" or \"negative\".

    if true, score will show as the double value, else will output string \"positive\" or \"negative\". Defaults false

  83. def setIncrementMultiplier(v: Double): SentimentDetector.this.type

    Multiplier for increment sentiments.

    Multiplier for increment sentiments. Defaults 2.0

  84. final def setInputCols(value: String*): SentimentDetector.this.type
    Definition Classes
    HasInputAnnotationCols
  85. final def setInputCols(value: Array[String]): SentimentDetector.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): SentimentDetector.this.type
    Definition Classes
    CanBeLazy
  87. def setNegativeMultiplier(v: Double): SentimentDetector.this.type

    Multiplier for negative sentiments.

    Multiplier for negative sentiments. Defaults -1.0

  88. final def setOutputCol(value: String): SentimentDetector.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  89. def setPositiveMultiplier(v: Double): SentimentDetector.this.type

    Multiplier for positive sentiments.

    Multiplier for positive sentiments. Defaults 1.0

  90. def setReverseMultiplier(v: Double): SentimentDetector.this.type

    Multiplier for revert sentiments.

    Multiplier for revert sentiments. Defaults -1.0

  91. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  92. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  93. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): SentimentDetectorModel
    Definition Classes
    SentimentDetectorAnnotatorApproach
  94. 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
  95. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  96. val uid: String
    Definition Classes
    SentimentDetector → Identifiable
  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 CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[SentimentDetectorModel]

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