Naive bayes, all of the elements in predict_proba output matrix are less than 0.5
No, your classifier can label text. It doesn't do it well but it is still almost 2 times better than random (for 7 classes, random will get you ~0.15 accuracy).

Looking at the test set is not enough. You need to create the same confusion matrix for you training set.

If the results you will get for the test set are similar in magnitude than maybe your model is too simple for the task or maybe you haven't trained it long enough.

If the results of the test set are good, than you might have a generalization problem (overfitting), which means that you need to increase the regularization during training. It also might mean that your training set comes from a different distribution than your test set.