Affect analysis of text using fuzzy semantic typing

By Pero Subasic, Alison K. Huettner. Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2000

355 citations recorded by the source corpus.

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

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8a26ded61b67cce241352ba8742cd8fa2541d605

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In Artificial Intelligence

Citations in the corpus

Evaluating sentiment in financial news articles (2012, background): “Some studies have attempted to classify emotions, including happiness, sadness, anger, horror, etc., instead of sentiments (Subasic and Huettner, 2001; Grefenstette, Qu et al., 2004; Mishne, 2005).”
SentiWS - A Publicly Available German-language Resource for Sentiment Analysis (2010, background): “Any given affect word may have multiple entries in an affect lexicon, differing by its part of speech and/or its category (Subasic and Huettner, 2001).”
CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON (2013, background): “There has also been some interesting work in visualizing emotions, for example that of Subasic and Huettner (2001), Kalra and Karahalios (2005), and Rashid et al. (2006).”
Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis (2004, background): “…classification can be divided into approaches that rely on semantic resources, such as a sentiment or affect lexicon (Nasukawa and Yi 2003, Subasic and Huettner 2001), or a large scale knowledge base (Liu et al 2003) on the one hand, and approaches that try to learn patterns directly from…”
A survey of multimodal sentiment analysis (2017, methodology): “Subasic and Huettner [66] proposed to apply fuzzy logic to cope with ambiguity.”
A survey on sentiment detection of reviews (2009, neutral): “…, Ostendorf, & Shriberg, 2003; Inkpen, Feiguina, & Hirst, 2004; Kobayashi, Inui, & Inui, 2001; Liu, Lieberman, & Selker, 2003; Raubern & Muller-Kogler, 2001; Riloff and Wiebe, 2003; Subasic & Huettner, 2001; Tong, 2001; Vegnaduzzo, 2004; Wiebe & Riloff, 2005; Wilson, Wiebe, & Hoffmann, 2005).”
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums (2008, methodology): “Score-based methods have also been used for affect analysis, where the affect features present within a message/document are scored based on their degree of intensity for a particular emotion class [Subasic and Huettner 2001].”
Affective News: The Automated Coding of Sentiment in Political Texts (2012, background): “As noted above, for some computational linguists, small differences across human coders are regarded as capturing real variation, or ambiguity, given the natural and structural ambiguity in categories of sentiment (Andreevskaia & Bergler, 2006; Subasic & Huettner, 2001).”
Mining WordNet for a Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses (2006, background): “…importance of various sentiment markers have crystallized in two main approaches: automatic assignment of weights based on some statistical criterion ((Hatzivassiloglou and McKeown, 1997; Turney and Littman, 2002; Kim and Hovy, 2004), and others) or manual annotation (Subasic and Huettner, 2001).”
Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales (2005, background): “…for text classifcation that have been considered, such as clause-level opinion strength (Wilson, Wiebe, and Hwa, 2004); affect types like disgust (Subasic and Huettner, 2001; Liu, Lieberman, and Selker, 2003); reading level (Collins-Thompson and Callan, 2004); and urgency or criticality…”
AffectiveSpace: Blending Common Sense and Affective Knowledge to Perform Emotive Reasoning (2009, methodology): “In the past, emotion extraction from text involved the implementation of diﬀer-ent techniques such as hand-crafted models [2], keyword spotting [3], fuzzy logic [4], lexical aﬃnity [5] and statistical methods [6].”
Mining the peanut gallery: opinion extraction and semantic classification of product reviews (2003, background): “Subasic and Huettner [20] manually constructed a lexicon associating words with affect categories, specifying an intensity (strength of affect level) and centrality (degree of relatedness to the category).”