A semantic approach to automated text sentiment analysis

The identification and characterization of evaluative stance in written language poses a unique set of cross-disciplinary challenges. Beginning with a review of relevant literature in linguistics and psychology, I trace recent interest in automated detection of author opinion in online product reviews, focusing on two main approaches: the semantic model, which is centered on deriving the semantic orientation (SO) of individual words and expressions, and machine learning classifiers, which rely on statistical information gathered from large corpora. To show the potential long-term advantages of the former, I describe the creation of an SO Calculator, highlighting relevant linguistic features such as intensification, negation, modality, and discourse structure, and devoting particular attention to the detection of genre in movie reviews, integrating machine classifier modules into my core semantic model. Finally, I discuss sentiment analysis in languages other than English, including Spanish and Chinese.