In this article, we introduce a compositional linguistic approach for attitude recognition in text. There are several aspects that distinguish our attitude analysis model (@AM) from other systems. First, our method classifies sentences using fine-grained attitude labels (nine for affective states, two for positive and negative judgment, and two for positive and negative appreciation), as compared against other methods that mainly focus on two sentiment categories (positive and negative) or basic emotions. Next, our @AM is based on the analysis of syntactic and dependence relations between words in a sentence, the compositionality principle, a novel linguistic approach based on the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. As distinct from the state-of-the-art approaches, the proposed method extensively deals with the semantics of terms, processes sentences of different complexity, handles not only correctly written text but also informal messages, and encodes the strength and the level of confidence of attitude through numerical values. The performance of our @AM was evaluated on data sets represented by sentences from different domains. @AM achieved a high level of accuracy on sentences from personal stories about life experiences, fairy tales, and news headlines, outperforming other methods on several measures.
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