# understanding of libsvm output

I applied libsvm to build a text classifier. The output looks like as follows:

w[a_b] = 0.006
w[a_c] = 0.032
w[a_ctif] = 0.000
w[a_cs] = 1.009
w[aa_d] = 0.000
w[a_e] = 0.001


The terms, e.g., a_b, and a_c, etc are the features generated for characterizing these documents. My question is how to understand these values corresponding to each term? The model output shows that all of these values are bigger than zero. Can I say that a positive file tends to have a term with higher weight?

SVM weights are calculated according to $\textbf{w}$ = $\sum_{i}$$\alpha_i$$y_i$$\textbf{x}_i$ (good overview here). These give insight into the importance of a feature to the classfier's ability to find the decision boundary between classes. Generally, if a feature has a positive weight, its presence contributes to the separating ability of the classifier, while if negative, it is the absence that is informative (see, for example, Lee et al. 2011).