Support vector machines (SVM) continue to remain a hugely popular machine learning technique, having made its way from the industry to classrooms and then back. In addition to several forms of regression, SVM is one of the techniques that forms the backbone of the multi-billion-dollar online ad targeting industry.
In academia, work such as that by T Joachim (https://www.cs.cornell.edu/people/tj/publications/joachims_98a.pdf) recommends support vector classifiers for text classification.
It's difficult to estimate whether it will be equally effective for us based on such literature, mainly due to a difference in the dataset and pre-processing steps. Let's give it a shot nevertheless:
from sklearn.svm import SVCsvc_clf ...