Subjective? Emotional? Emotive?: Language Combinatorics based Automatic Detection of Emotionally Loaded Sentences

In this paper presents our research in automatic detection of emotionally loaded, or emotive sentences. We define the problem from a linguistic point of view assuming that emotive sentences stand out both lexically and grammatically. To verify this assumption we prepare a text classification experiment. In the experiment we apply language combinatorics approach to automatically extract emotive patterns from training sentences. The applied approach allows automatic extraction of not only widely used unigrams (tokens), or n-grams, but also more sophisticated patterns with disjointed elements. The results of experiments are explained with the use of means such as standard Precision, Recall and balanced F-score. The algorithm also provides a refined list of most frequent sophisticated patterns typical for both emotive and non-emotive context. The method reached results comparable to the state of the art, while the fact that it is fully automatic makes it more efficient and language independent.