I'm curious what kind of strategies people are using for deciding how to remove unimportant terms from a Text Explorer analysis.
I am working with a customer and they want to reduce the number of terms to something more manageable, and something that explains more of the percent variation when the do latent sematic analyis (the Cum Percent reported under the singluar value).
They are interatively doing LSA then RotatedTopic Analysis for the top x% of the Singular Value Cum Percent (they want to get to 70%), then removing terms.
How do we remove terms in a smart way?
I suggested they use the absolute value of each term for each topic and pick the lowest scores (cause those words aren't contributing to ANY topic).
They were also using Gen Reg to model topics against an outcome variable to find topics not important to that outcome, and removing top terms from zero-ed topics (with the LASSO). I'm less sure about this approach.
Any other thoughts or ideas out there? Should i post this to the public community?