Vector Space Models for the Classification of Short Messages on Social Network Services

In this chapter we review vector space models to propose a new one based on the Jensen-Shannon divergence with the goal of classifying ignored short messages on a social network service. We assume that ignored messages are those published ones that were not interacted with. Our goal then is to attempt to classify messages to be published as ignored to discard them from a set messages that can be used by a recommender system. To evaluate our model, we conduct experiments comparing different models on a Twitter dataset with more than 13,000 Twitter accounts. Results show that our best model tested obtained an average accuracy of 0.77, compared to 0.74 from a model from the literature. Similarly, this method obtained an average precision of 0.74 compared to 0.58 from the second best performing model.