Add bigrams to baseline algorithms

Right now unigram linear SVMs have a F1 score of 0.918 and bigram linear SVMs
have a F1 score of 0.933. The latter is the best-performing baseline algorithm.
Tune hyperparameters of bigram linear SVM

There isn't a huge difference in F1 score when C (regularization parameter) is
changed between 1, 10 and 100, and when the loss is changed between L1 and L2.
It's probably because the feature space is already linearly separable.
Change SVM implementation to one without SGD

SGD = stochastic gradient descent. We move from
sklearn.linear_classifier.SGDClassifier to sklearn.svm.LinearSVM because the
latter seems to give better performance.  Possibly because we didn't give
enough training epochs to the SGD classifier.