Closed wzjjia closed 5 years ago
Sorry, but I am not aware of any way to train your own model.
As I understand, the shipped model is trained using Sentiment Treebank
https://nlp.stanford.edu/sentiment/treebank.html
So you can your cases there and next model version should be better.
You also may want to search issues on original repo https://github.com/stanfordnlp/CoreNLP or questions on SO https://stackoverflow.com/questions/tagged/stanford-nlp
Seems that there is a way to do this...
"If you sometimes like to go to the movies to have fun Wasabi is a good place to start." How can I convert the above statement to the following format? (3 (3 (2 If) (3 (2 you) (3 (2 sometimes) (2 (2 like) (3 (2 to) (3 (3 (2 go) (2 (2 to) (2 (2 the) (2 movies)))) (3 (2 to) (3 (2 have) (4 fun))))))))) (2 (2 ,) (2 (2 Wasabi) (3 (3 (2 is) (2 (2 a) (2 (3 good) (2 (2 place) (2 (2 to) (2 start)))))) (2 .)))))
@wzjjia I'm new to this as well but it appears BuildBinarizedDataset is the one to use. https://github.com/stanfordnlp/CoreNLP/issues/296 https://github.com/stanfordnlp/CoreNLP/issues/32#issuecomment-61037436 https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/discussion/12304
What is not clear is how do you break down each sentence into its phrases and then assign sentiment scores for them.
For example why for this sentence.
Today is not a good day.
How do we know it has the following phrases.
good
good day
a good day
Perhaps @rstjobs @J38 @manning can comment.
Here's the problem How do we know it has the following phrases. good good day a good day
Wow seriously?? no one responded to this. That just shows the amount of support CoreNLP has.
Sorry, this is the wrong repo for such questions Please ask on StackOverflow or open new issue in official repo https://github.com/stanfordnlp/CoreNLP
Opps sorry didn't look at the repo name
How does the Sentiment analysis function train me to provide a sentence like, "I'm happy today" and I'm going to train him to be positive