Abstract: Identification of augmented features based on a Bayesian analysis of a text document is disclosed. One example is a system including a document processing module, a feature processing module, and a feature generation module. The document processing module receives a text document via a processor. The feature processing module automatically identifies, based on a Bayesian analysis of the text document, a plurality of augmented features in the text document, the plurality of augmented features including at least one of local, sectional, and document-level features of the text document, and extracts, via the processor, the identified plurality of augmented features from the text document. The feature generation module generates, via the processor, a feature representation of the text document based on the extracted plurality of augmented features.

Abstract: A computing device includes at least one processor and a sentiment analysis module. The sentiment analysis module is to, for each document set of a plurality of document sets, determine a distribution of sentiment classes for documents included in the document set. The sentiment analysis module is also to select, from the plurality of document sets, a first document set for analyzing a target document, and set a prior distribution of sentiment classes of the target document equal to the distribution of sentiment classes for documents included in the first document set. The sentiment analysis module is also to perform a Bayesian classification of the target document using a training data set and the prior distribution of sentiment classes of the target document, and determine a sentiment class for the target document based on the Bayesian classification.