Description
Classify sentiment in tweets as negative or positive using Universal Sentence Encoder embeddings.
Predicted Entities
positive, negative
Live Demo Open in Colab Download Copy S3 URI
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
use = UniversalSentenceEncoder.pretrained('tfhub_use', lang="en") \
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
classifier = SentimentDLModel().pretrained('sentimentdl_use_twitter')\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("sentiment")
nlp_pipeline = Pipeline(stages=[document_assembler,
use,
classifier
])
l_model = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
annotations = l_model.fullAnnotate(["im meeting up with one of my besties tonight! Cant wait!! - GIRL TALK!!", "is upset that he can't update his Facebook by texting it... and might cry as a result School today also. Blah!"])
import nlu
nlu.load("en.sentiment.twitter.dl").predict("""is upset that he can't update his Facebook by texting it... and might cry as a result School today also. Blah!""")
Results
| | document | sentiment |
|---:|:---------------------------------------------------------------------------------------------------------------- |:------------|
| 0 | im meeting up with one of my besties tonight! Cant wait!! - GIRL TALK!! | positive |
| 1 | is upset that he can't update his Facebook by texting it... and might cry as a result School today also. Blah! | negative |
Model Information
| Model Name: | sentimentdl_use_twitter |
| Compatibility: | Spark NLP 2.7.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [sentiment] |
| Language: | en |
| Dependencies: | tfhub_use |
Data Source
Trained on Sentiment140 dataset comprising of 1.6M tweets. https://www.kaggle.com/kazanova/sentiment140
Benchmarking
loss: 7930.071 - acc: 0.80694044 - val_acc: 80.00508 - batches: 16000