Sentiment Analysis, missclassification
Chertushkin opened this issue · 1 comments
Chertushkin commented
Pipeine from Sentiment_rb.ipynb missclassifies obvious sentences
Steps to Reproduce
- Open https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/jupyter/annotation/english/dictionary-sentiment
- Load the pipeline
analyze_sentiment_ml
- Try to annotate
Harry Potter is a good movie
. You will see that sentiment is positive. That's correct. - Try to annotate
Harry Potter is a bad movie
. You will see that sentiment is still positive. That's a mistake. - Also, try to annotate
Harry Potter
. The model will classify it as negative :)
Your Environment
- Spark-NLP version: 2.0.3
- Apache Spark version: 2.4.1
- Operating System and version: Docker
- Deployment (Docker, Jupyter, Scala, pip, conda, etc.): I have tried your actual Docker container.
maziyarpanahi commented
We tried to re-train the sentiment models in 2.1.0
, in case it doesn't perform well on common sentences, please re-open this issue.