class SentimentDLApproach extends AnnotatorApproach[SentimentDLModel] with ParamsAndFeaturesWritable

Trains a SentimentDL, an annotator for multi-class sentiment analysis.

In natural language processing, sentiment analysis is the task of classifying the affective state or subjective view of a text. A common example is if either a product review or tweet can be interpreted positively or negatively.

For the instantiated/pretrained models, see SentimentDLModel.

Notes:

For extended examples of usage, see the Spark NLP Workshop and the SentimentDLTestSpec.

Example

In this example, sentiment.csv is in the form

text,label
This movie is the best movie I have watched ever! In my opinion this movie can win an award.,0
This was a terrible movie! The acting was bad really bad!,1

The model can then be trained with

import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.UniversalSentenceEncoder
import com.johnsnowlabs.nlp.annotators.classifier.dl.{SentimentDLApproach, SentimentDLModel}
import org.apache.spark.ml.Pipeline

val smallCorpus = spark.read.option("header", "true").csv("src/test/resources/classifier/sentiment.csv")

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val useEmbeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("sentence_embeddings")

val docClassifier = new SentimentDLApproach()
  .setInputCols("sentence_embeddings")
  .setOutputCol("sentiment")
  .setLabelColumn("label")
  .setBatchSize(32)
  .setMaxEpochs(1)
  .setLr(5e-3f)
  .setDropout(0.5f)

val pipeline = new Pipeline()
  .setStages(
    Array(
      documentAssembler,
      useEmbeddings,
      docClassifier
    )
  )

val pipelineModel = pipeline.fit(smallCorpus)
See also

ClassifierDLApproach for general single-class classification

MultiClassifierDLApproach for general multi-class classification

Linear Supertypes
ParamsAndFeaturesWritable, HasFeatures, AnnotatorApproach[SentimentDLModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[SentimentDLModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. SentimentDLApproach
  2. ParamsAndFeaturesWritable
  3. HasFeatures
  4. AnnotatorApproach
  5. CanBeLazy
  6. DefaultParamsWritable
  7. MLWritable
  8. HasOutputAnnotatorType
  9. HasOutputAnnotationCol
  10. HasInputAnnotationCols
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Instance Constructors

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

    uid

    required uid for storing 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. 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 _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): SentimentDLModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. val batchSize: IntParam

    Batch size (Default: 64)

  12. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    SentimentDLApproachAnnotatorApproach
  13. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  14. final def clear(param: Param[_]): SentimentDLApproach.this.type
    Definition Classes
    Params
  15. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  16. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  17. final def copy(extra: ParamMap): Estimator[SentimentDLModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  18. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  19. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  20. val description: String
    Definition Classes
    SentimentDLApproachAnnotatorApproach
  21. val dropout: FloatParam

    Dropout coefficient (Default: 0.5f)

  22. val enableOutputLogs: BooleanParam

    Whether to output to annotators log folder (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. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  28. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  29. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  30. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  31. final def fit(dataset: Dataset[_]): SentimentDLModel
    Definition Classes
    AnnotatorApproach → Estimator
  32. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[SentimentDLModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  33. def fit(dataset: Dataset[_], paramMap: ParamMap): SentimentDLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  34. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): SentimentDLModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  35. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  36. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  37. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  40. def getBatchSize: Int

  41. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  42. def getConfigProtoBytes: Option[Array[Byte]]

  43. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  44. def getDropout: Float

  45. def getEnableOutputLogs: Boolean

  46. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  47. def getLabelColumn: String

  48. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  49. def getLr: Float

  50. def getMaxEpochs: Int

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  53. def getOutputLogsPath: String

  54. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  55. def getThreshold: Float

  56. def getThresholdLabel: String

  57. def getValidationSplit: Float

  58. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  59. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  60. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  61. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  62. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. val inputAnnotatorTypes: Array[AnnotatorType]

    Input Annotator Types: SENTENCE_EMBEDDINGS

    Input Annotator Types: SENTENCE_EMBEDDINGS

    Definition Classes
    SentimentDLApproachHasInputAnnotationCols
  64. 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
  65. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  66. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  67. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  68. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  69. val labelColumn: Param[String]

    Column with label per each document

  70. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  71. def loadSavedModel(): TensorflowWrapper
  72. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  73. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  80. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. val lr: FloatParam

    Learning Rate (Default: 5e-3f)

  85. val maxEpochs: IntParam

    Maximum number of epochs to train (Default: 10)

  86. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  87. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  88. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  90. def onTrained(model: SentimentDLModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  91. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  92. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  93. val outputAnnotatorType: String

    Output Annotator Types: CATEGORY

    Output Annotator Types: CATEGORY

    Definition Classes
    SentimentDLApproachHasOutputAnnotatorType
  94. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  95. val outputLogsPath: Param[String]

    Folder path to save training logs (Default: "")

  96. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  97. val randomSeed: IntParam

    Random seed for shuffling the dataset

  98. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  99. def set[T](feature: StructFeature[T], value: T): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  100. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  101. def set[T](feature: SetFeature[T], value: Set[T]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def set[T](feature: ArrayFeature[T], value: Array[T]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. final def set(paramPair: ParamPair[_]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  104. final def set(param: String, value: Any): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  105. final def set[T](param: Param[T], value: T): SentimentDLApproach.this.type
    Definition Classes
    Params
  106. def setBatchSize(batch: Int): SentimentDLApproach.this.type

  107. def setConfigProtoBytes(bytes: Array[Int]): SentimentDLApproach.this.type

  108. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  110. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  111. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. final def setDefault(paramPairs: ParamPair[_]*): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  113. final def setDefault[T](param: Param[T], value: T): SentimentDLApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  114. def setDropout(dropout: Float): SentimentDLApproach.this.type

  115. def setEnableOutputLogs(enableOutputLogs: Boolean): SentimentDLApproach.this.type

  116. final def setInputCols(value: String*): SentimentDLApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  117. final def setInputCols(value: Array[String]): SentimentDLApproach.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  118. def setLabelColumn(column: String): SentimentDLApproach.this.type

  119. def setLazyAnnotator(value: Boolean): SentimentDLApproach.this.type
    Definition Classes
    CanBeLazy
  120. def setLr(lr: Float): SentimentDLApproach.this.type

  121. def setMaxEpochs(epochs: Int): SentimentDLApproach.this.type

  122. final def setOutputCol(value: String): SentimentDLApproach.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  123. def setOutputLogsPath(path: String): SentimentDLApproach.this.type

  124. def setRandomSeed(seed: Int): SentimentDLApproach.this.type

    Random seed

  125. def setThreshold(threshold: Float): SentimentDLApproach.this.type

  126. def setThresholdLabel(label: String): SentimentDLApproach.this.type

  127. def setValidationSplit(validationSplit: Float): SentimentDLApproach.this.type

  128. def setVerbose(verbose: Level): SentimentDLApproach.this.type

  129. def setVerbose(verbose: Int): SentimentDLApproach.this.type

  130. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  131. val threshold: FloatParam

    The minimum threshold for the final result otherwise it will be either neutral or the value set in thresholdLabel (Default: 0.6f)

  132. val thresholdLabel: Param[String]

    In case the score is less than threshold, what should be the label (Default: "neutral")

  133. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  134. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): SentimentDLModel
    Definition Classes
    SentimentDLApproachAnnotatorApproach
  135. 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
  136. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  137. val uid: String
    Definition Classes
    SentimentDLApproach → Identifiable
  138. 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
  139. val validationSplit: FloatParam

    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f).

    Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f). The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

  140. val verbose: IntParam

    Level of verbosity during training (Default: Verbose.Silent.id)

  141. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  142. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  143. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  144. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[SentimentDLModel]

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