Description
Automatically identify Joy, Surprise, Fear, Sadness emotions in Tweets.
Predicted Entities
surprise, sadness, fear, joy
Live Demo Open in Colab Download
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
use = UniversalSentenceEncoder.pretrained('tfhub_use', lang="en") \
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
document_classifier = ClassifierDLModel.pretrained('classifierdl_use_emotion', 'en') \
.setInputCols(["document", "sentence_embeddings"]) \
.setOutputCol("class")
nlpPipeline = Pipeline(stages=[document_assembler, 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 |
| Compatibility: | Spark NLP 2.7.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [class] |
| Language: | en |
| Dependencies: | tfhub_use |
Data Source
This model is trained on multiple datasets inlcuding youtube comments, twitter and ISEAR dataset.
Benchmarking
fear 0.78 0.67 0.72 2253
joy 0.71 0.68 0.69 3000
sadness 0.69 0.73 0.71 3075
surprise 0.67 0.73 0.70 3067
accuracy 0.71 11395
macro avg 0.71 0.70 0.71 11395
weighted avg 0.71 0.71 0.71 11395