A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Creative language retrieval: a robust hybrid of information retrieval and linguistic creativity
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Tracking sentiment in mail: how genders differ on emotional axes
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
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Models of sentiment analysis in text require an understanding of what kinds of sentiment-bearing language are generally used to describe specific topics. Thus, fine-grained sentiment analysis requires both a topic lexicon and a sentiment lexicon, and an affective mapping between both. For instance, when one speaks disparagingly about a city (like London, say), what aspects of city does one generally focus on, and what words are used to disparage those aspects? As when we talk about the weather, our language obeys certain familiar patterns - what we might call clichés and stereotypes - when we talk about familiar topics. In this paper we describe the construction of an affective stereotype lexicon, that is, a lexicon of stereotypes and their most salient affective qualities. We show, via a demonstration system called MOODfinger, how this lexicon can be used to underpin the processes of affective query expansion and summarization in a system for retrieving and organizing news content from the Web. Though we adopt a simple bipolar +/- view of sentiment, we show how this stereotype lexicon allows users to coin their own nuanced moods on demand.