Affect analysis of text using fuzzy semantic typing

We propose a novel, convenient fusion of natural language processing and fuzzy logic techniques for analyzing the affect content in free text. Our main goals are fast analysis and visualization of affect content for decision making. The main linguistic resource for fuzzy semantic typing is the fuzzy-affect lexicon, from which other important resources—the fuzzy thesaurus and affect category groups—are generated. Free text is tagged with affect categories from the lexicon and the affect categories’ centralities and intensities are combined using techniques from fuzzy logic to produce affect sets—fuzzy sets representing the affect quality of a document. We show different aspects of affect analysis using news content and movie reviews. Our experiments show a good correspondence between affect sets and human judgments of affect content. We ascribe this to the representation of ambiguity in our fuzzy affect lexicon and the ability of fuzzy logic to deal successfully with the ambiguity of words in a natural language. Planned extensions of the system include personalized profiles for Web-based content dissemination, fuzzy retrieval, clustering, and classification.