Closed ManuelBanza closed 1 year ago
There's notebook by Hassan Amin on Kaggle explaining unsupervised sentiment analysis using Vader https://www.kaggle.com/hassanamin/unsupervised-sentiment-analysis-using-vader https://pypi.org/project/vaderSentiment/ https://github.com/cjhutto/vaderSentiment
it can assess sentiment based on punctuation, capitalization, conjunctions and slang even on emoji and emoticon is it possible to implement it on NLP modules? it would be really nice if it can automatically label data to 'positive', 'negative' or 'neutral' category based on the highest sentiment score
We are no more supporting the nlp
and arules
module in PyCaret due to lack to resources. Closing the issue now.
Hi,
In the NLP Model when we do the assign model a dataset is returned with topics and the dominant_topic. Is it possible to also bring the sentiment score and polarity?
Cheers!