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I want to use the dictionary based sentiment approach for texts by using a self-created dictionary.
The dictionary sentiment approach does not account for negations as far as I know.
For example: Today I was not really productive.
The word "productive" is positive but because of the "not" the whole sentence has a negative meaning instead of positive.
How does the dictionary based sentiment approach of Rapidminer account for this issue?
All the best,
welcome to the community! I've written the operator myself and I am very happy that you use it. The current version of the operator does not handle negations. Negations are in general a tricky thing also in more sophisticated approaches like SVMs. I am not sure how this dictionary based approach could handle this. If you have a solution in mind I am happy to have a look if we can implement this.
first of all: happy new year. Thank you very much for your reply.
One possible solution to account for negations might be to include another "dictionary" of self-created word list which consists of negation words.
This word list is applied to the same text corpus as the sentiment dictionary in the following way:
Do you think this might work?
i've implemented this (without counting but same idea). I will share a preview version of this via e-mail since i need to write some docs and it needs to run through our internal code-quality process before it appears on market place.