Description
A pre-trained pipeline to analyze sentiment in tweets and classify them into ‘positive’ and ‘negative’ classes using Universal Sentence Encoder embeddings
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How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("analyze_sentimentdl_use_twitter", lang = "en")
result = pipeline.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!"])
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: | analyze_sentimentdl_use_twitter |
| Type: | pipeline |
| Compatibility: | Spark NLP 2.7.1+ |
| Edition: | Official |
| Language: | en |
Included Models
tfhub_use, sentimentdl_use_twitter