I am trying to clean the support ticket description to help the cluster command on the clusterization.
I have found the "Splunk NLP Text Analytics app" that permits to use the
cleantext function, but it allows to define single words to clean the text, basically because the analyzed text is tokenized.
Do you know if there is a more efficient way than the one indicated below to perform a cleanup on a text cleaning the defined sentences instead of single words?
index ="myindex" host="myhost" sourcetype="mysourcetype" source="mysource" | eval description=lower(description) | eval description= replace(description,"the user reports", "") | eval description= replace(description,"the user complains that", "") | ....
I would like to create a lookup table that join over the sentences and clean them from the description field.
Do you have any suggestion or better approach?
Thanks a lot,
I can think of several solutions but only one as an extensible example.
Basically, build a Data Model that does this for using calculated fields. Because otherwise, when you do it while running your search, searches may get slow depending on the overall load and amount of data.
This way, you offload this logic to the data model (and accelerate to be even faster) and build your search on top of the calculated fields of the data model.
I believe this is a reliable solution. Thanks a lot!
The cleantext command does offer an option to remove custom stopwords using customstopwords=comma-seperated-list, so in your example you could add customstopwords="user,report,complain" as the other words are standard stopwords. Or you could remove the particular phrases before running the command. Not sure if that is what you are after.
Thanks for your feedback, yes I was using this option, but you can't join over a lookup in this way. You can just define a list of words (not sentences) to be excluded