Combining Textual Pre-game Reports and Statistical Data for Predicting Success in the National Hockey League

In this paper, we create meta-classifiers to forecast success in the National Hockey League. We combine three classifiers that use various types of information. The first one uses as features numerical data and statistics collected during previous games. The last two classifiers use pre-game textual reports: one classifier uses words as features (unigrams, bigrams and trigrams) in order to detect the main ideas expressed in the texts and the second one uses features based on counts of positive and negative words in order to detect the opinions of the pre-game report writers. Our results show that meta classifiers that use the two data sources combined in various ways obtain better prediction accuracies than classifiers that use only numerical data or only textual data.