Abstract
Due to prevalence of textual messages on internet, automated opinion analysis becomes importance and raised much attention of researchers. However, to achieve high performance of automated opinion analysis is not easy since without profound models of human intentions and mental attitudes it is hard for computational methods to infer from embedded messages. Modality concepts are usually associated with expressing human opinions and attitudes but whose accurate inference is still not yet computational feasible. This paper attempts to investigate how different modalities such as deontic and epistemic concepts can be automated classified from textual messages that are associated with modal sentences. The research adopts a machine learning algorithms, employ ConceptNet to augment the selection of features and SVM as supervised learning to train a classifier and uses C4.5 and simple perceptron to define deontic and epistemic models. The cross validation learning experiments take 844 examples as training and test data set and measure the performance in classifying the sentences into six different modal categories. We reach performance of F-score up to 71.2% at this preliminary research and subsequent discussions follow.