Little work to date in sentiment analysis (classifying texts by 'positive' or 'negative' orientation) has attempted to use fine-grained semantic distinctions in features used for classification. We present a new method for sentiment classification based on extracting and analyzing appraisal groups such as "very good" or "not terribly funny". An appraisal group is represented as a set of attribute values in several task-independent semantic taxonomies, based on Appraisal Theory. Semi-automated methods were used to build a lexicon of appraising adjectives and their modifiers. We classify movie reviews using features based upon these taxonomies combined with standard "bag-of-words" features, and report state-of-the-art accuracy of 90.2%. In addition, we find that some types of appraisal appear to be more significant for sentiment classification than others.

Most text analysis and retrieval work to date has focused on the topic of a text; that is, what it is about. However, a text also contains much useful information in its style, or how it is written. This includes information about its author, its purpose, feelings it is meant to evoke, and more. This article develops a new type of lexical feature for use in stylistic text classification, based on taxonomies of various semantic functions of certain choice words or phrases. We demonstrate the usefulness of such features for the stylistic text classification tasks of determining author identity and nationality, the gender of literary characters, a text's sentiment (positive/ negative evaluation), and the rhetorical character of scientific journal articles. We further show how the use of functional features aids in gaining insight about stylistic differences among different kinds of texts.

Much interesting text on the web consists largely of opinionated or evaluative text, as opposed to directly informative text. The new field of 'sentiment analysis' seeks to characterize such aspects of natural language text, as opposed to just the bare facts. We suggest that 'appraisal expression extraction' should be viewed as a fundamental task for sentiment analysis. We define an 'appraisal expression' to be a piece of text expressing some evaluative stance towards a particular object. The task is to find these elements and characterize the type and orientation (positive or negative) of the evaluative stance, as well as its target and possibly its source. Potential applications of these methods include new approaches to the now-traditional tasks of sentiment classification and opinion mining, as well as possibly for adversarial textual analysis and intention detection for intelligence applications.

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