fitcsvm Feature Coefficients Meaning

I'm running a series of SVM classifiers for a binary classification problem, and am getting very nice results as far as classification accuracy.

The next step of my analysis is to understand how the different features contribute to the classification. According to the documentation, Matlab's fitcsvm function returns a class, SVMModel, which has a field called "Beta", defined as:

Numeric vector of trained classifier coefficients from the primal linear problem. Beta has length equal to the number of predictors (i.e., size(SVMModel.X,2)).

I'm not quite sure how to interpret these values. I assume higher values represent a greater contribution of a given feature to the support vector? What do negative weights mean? Are these weights somehow analogous to beta parameters in a linear regression model?

Thanks for any help and suggestions