Description
Automatically identify Joy, Surprise, Fear, Sadness in Tweets using out pretrained Spark NLP DL classifier.
Predicted Entities
surprise, sadness, fear, joy
Live Demo
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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_emotion', '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('@Mira I just saw you on live t.v!!')
Results
+------------------------------------------------------------------------------------------------+------------+
|document |class |
+------------------------------------------------------------------------------------------------+------------+
|@Mira I just saw you on live t.v!! | joy |
+------------------------------------------------------------------------------------------------+------------+
Model Information
| Model Name | classifierdl_use_emotion |
| 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 | tfhub_use |
Data Source
This model is trained on multiple datasets inlcuding youtube comments, twitter and ISEAR dataset.
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