Packages

class SentimentDetector extends AnnotatorApproach[SentimentDetectorModel]

Trains a rule based sentiment detector, which calculates a score based on predefined keywords.

A dictionary of predefined sentiment keywords must be provided with setDictionary, where each line is a word delimited to its class (either positive or negative). The dictionary can be set in either in the form of a delimited text file or directly as an ExternalResource.

By default, the sentiment score will be assigned labels "positive" if the score is >= 0, else "negative". To retrieve the raw sentiment scores, enableScore needs to be set to true.

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

Example

In this example, the dictionary default-sentiment-dict.txt has the form of

...
cool,positive
superb,positive
bad,negative
uninspired,negative
...

where each sentiment keyword is delimited by ",".

import spark.implicits._
import com.johnsnowlabs.nlp.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.Tokenizer
import com.johnsnowlabs.nlp.annotators.Lemmatizer
import com.johnsnowlabs.nlp.annotators.sda.pragmatic.SentimentDetector
import com.johnsnowlabs.nlp.util.io.ReadAs
import org.apache.spark.ml.Pipeline

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

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

val lemmatizer = new Lemmatizer()
  .setInputCols("token")
  .setOutputCol("lemma")
  .setDictionary("src/test/resources/lemma-corpus-small/lemmas_small.txt", "->", "\t")

val sentimentDetector = new SentimentDetector()
  .setInputCols("lemma", "document")
  .setOutputCol("sentimentScore")
  .setDictionary("src/test/resources/sentiment-corpus/default-sentiment-dict.txt", ",", ReadAs.TEXT)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  lemmatizer,
  sentimentDetector,
))

val data = Seq(
  "The staff of the restaurant is nice",
  "I recommend others to avoid because it is too expensive"
).toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("sentimentScore.result").show(false)
+----------+  //  +------+ for enableScore set to true
|result    |  //  |result|
+----------+  //  +------+
|[positive]|  //  |[1.0] |
|[negative]|  //  |[-2.0]|
+----------+  //  +------+
See also

ViveknSentimentApproach for an alternative approach to sentiment extraction

Linear Supertypes
AnnotatorApproach[SentimentDetectorModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[SentimentDetectorModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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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
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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 (Default: -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
    SentimentDetector → AnnotatorApproach
  16. val dictionary: ExternalResourceParam

    Delimited file with a list sentiment tags per word (either positive or negative).

    Delimited file with a list sentiment tags per word (either positive or negative). Requires 'delimiter' in options.

    Example

    cool,positive
superb,positive
bad,negative
uninspired,negative

    where the 'delimiter' options was set with Map("delimiter" -> ",")

  17. val enableScore: BooleanParam

    If true, score will show as the double value, else will output string "positive" or "negative" (Default: 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: Seq[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 (Default: 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
    SentimentDetector → HasInputAnnotationCols
  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 (Default: -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 optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  69. val outputAnnotatorType: AnnotatorType

    Output annotation type : SENTIMENT

    Output annotation type : SENTIMENT

    Definition Classes
    SentimentDetector → HasOutputAnnotatorType
  70. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  71. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  72. val positiveMultiplier: DoubleParam

    Multiplier for positive sentiments (Default: 1.0)

  73. val reverseMultiplier: DoubleParam

    Multiplier for revert sentiments (Default: -1.0)

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

    Multiplier for decrement sentiments (Default: -2.0)

  79. final def setDefault(paramPairs: ParamPair[_]*): SentimentDetector.this.type
    Attributes
    protected
    Definition Classes
    Params
  80. final def setDefault[T](param: Param[T], value: T): SentimentDetector.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  81. 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

  82. 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

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

    If true, score will show as the double value, else will output string "positive" or "negative" (Default: false)

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

    Multiplier for increment sentiments (Default: 2.0)

  85. final def setInputCols(value: String*): SentimentDetector.this.type
    Definition Classes
    HasInputAnnotationCols
  86. 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
  87. def setLazyAnnotator(value: Boolean): SentimentDetector.this.type
    Definition Classes
    CanBeLazy
  88. def setNegativeMultiplier(v: Double): SentimentDetector.this.type

    Multiplier for negative sentiments (Default: -1.0)

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

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

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

    Multiplier for positive sentiments (Default: 1.0)

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

    Multiplier for revert sentiments (Default: -1.0)

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

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

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