Sarcasm Classifier

Description

Classify if a text contains sarcasm.

Predicted Entities

normal, sarcasm

Live Demo
Open in Colab
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How to use


documentAssembler = DocumentAssembler()\
  .setInputCol("text")\
  .setOutputCol("document")

use = UniversalSentenceEncoder.pretrained(lang="en") \
  .setInputCols(["document"])\
  .setOutputCol("sentence_embeddings")


document_classifier = ClassifierDLModel.pretrained('classifierdl_use_sarcasm', 'en') \
  .setInputCols(["document", "sentence_embeddings"]) \
  .setOutputCol("class")

nlpPipeline = Pipeline(stages=[documentAssembler, use, document_classifier])

light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))

annotations = light_pipeline.fullAnnotate('If I could put into words how much I love waking up at am on Tuesdays I would')

Results

+--------------------------------------------------------------------------------------------------------+------------+
|document                                                                                                |class       |
+--------------------------------------------------------------------------------------------------------+------------+
|If I could put into words how much I love waking up at am on Tuesdays I would                           | sarcasm    |
+--------------------------------------------------------------------------------------------------------+------------+

Model Information

Model Name classifierdl_use_sarcasm
Model Class ClassifierDLModel
Spark Compatibility 2.5.3
Spark NLP Compatibility 2.4
License open source
Edition public
Input Labels [document, sentence_embeddings]
Output Labels [class]
Language en
Upstream Dependencies with tfhub_use

Data Source

This model is trained on the sarcam detection dataset. https://github.com/MirunaPislar/Sarcasm-Detection/tree/master/res/datasets/riloff