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com.johnsnowlabs.nlp.annotators.sda.vivekn

ViveknSentimentApproach

class ViveknSentimentApproach extends AnnotatorApproach[ViveknSentimentModel] with ViveknSentimentUtils

Trains a 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".

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.

The training data needs to consist of a column for normalized text and a label column (either "positive" or "negative").

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

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.annotators.Normalizer
import com.johnsnowlabs.nlp.annotators.sda.vivekn.ViveknSentimentApproach
import com.johnsnowlabs.nlp.Finisher
import org.apache.spark.ml.Pipeline

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

val token = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val normalizer = new Normalizer()
  .setInputCols("token")
  .setOutputCol("normal")

val vivekn = new ViveknSentimentApproach()
  .setInputCols("document", "normal")
  .setSentimentCol("train_sentiment")
  .setOutputCol("result_sentiment")

val finisher = new Finisher()
  .setInputCols("result_sentiment")
  .setOutputCols("final_sentiment")

val pipeline = new Pipeline().setStages(Array(document, token, normalizer, vivekn, finisher))

val training = Seq(
  ("I really liked this movie!", "positive"),
  ("The cast was horrible", "negative"),
  ("Never going to watch this again or recommend it to anyone", "negative"),
  ("It's a waste of time", "negative"),
  ("I loved the protagonist", "positive"),
  ("The music was really really good", "positive")
).toDF("text", "train_sentiment")
val pipelineModel = pipeline.fit(training)

val data = Seq(
  "I recommend this movie",
  "Dont waste your time!!!"
).toDF("text")
val result = pipelineModel.transform(data)

result.select("final_sentiment").show(false)
+---------------+
|final_sentiment|
+---------------+
|[positive]     |
|[negative]     |
+---------------+
See also

SentimentDetector for an alternative approach to sentiment detection

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
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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
    ViveknSentimentApproach → AnnotatorApproach
  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 (Default: Disabled with -1)

  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: Seq[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 (Default: Disabled with -1)

  32. def getImportantFeatureRatio(v: Double): Double

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

  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
    ViveknSentimentApproach → HasInputAnnotationCols
  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 optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  71. val outputAnnotatorType: AnnotatorType

    Output annotator type : SENTIMENT

    Output annotator type : SENTIMENT

    Definition Classes
    ViveknSentimentApproach → HasOutputAnnotatorType
  72. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  73. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  74. val pruneCorpus: IntParam

    Removes unfrequent scenarios from scope.

    Removes unfrequent scenarios from scope. The higher the better performance (Default: 1)

  75. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  76. 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"

  77. final def set(paramPair: ParamPair[_]): ViveknSentimentApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  78. final def set(param: String, value: Any): ViveknSentimentApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  79. final def set[T](param: Param[T], value: T): ViveknSentimentApproach.this.type
    Definition Classes
    Params
  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[org.apache.spark.ml]
    Definition Classes
    Params
  82. def setFeatureLimit(v: Int): ViveknSentimentApproach.this.type

    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 (Default: 0.5)

  84. final def setInputCols(value: String*): ViveknSentimentApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  85. 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 setPruneCorpus(value: Int): ViveknSentimentApproach.this.type

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

  89. 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'

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

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

  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]): ViveknSentimentModel
    Definition Classes
    ViveknSentimentApproach → AnnotatorApproach
  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
    ViveknSentimentApproach → Identifiable
  97. val unimportantFeatureStep: DoubleParam

    Proportion to lookahead in unimportant features (Default: 0.025)

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

Inherited from ViveknSentimentUtils

Inherited from AnnotatorApproach[ViveknSentimentModel]

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

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