Previous work in traditional text classification and its variants — such as sentiment analysis — has achieved successful results by using the bag-of-words representation; that is, by treating text as a collection of words with no interdependencies, training a classifier on a large feature set of word unigrams which appear in the corpus.

Defining features in this manner allows us to both explore the bag-of-words representation as well as use groups of n-grams as features, which we believed would be a better fit for this problem.

In experiments based on the bag-of-words model, we only consider an absolute frequency threshold, whereas in later experiments, we also take into account the relative frequency ratio threshold.

Our baseline approach to both problems is to use a bag-of-words model of the contribution, and use machine learning for classification.

We build a contextual feature space, described in section 4.2, to enhance our baseline bag-of-words model.

This is a distinction that a bag-of-words model would have difficulty with.