Computing Affect in Metaphors

Abstract

This article describes a novel approach to automated determination of affect associated with metaphorical language. Affect in language is understood to mean the attitude toward a topic that a writer attempts to convey to the reader by using a particular metaphor. This affect, which we will classify as positive, negative or neutral with various degrees of intensity, may arise from the target of the metaphor, from the choice of words used to describe it, or from other elements in its immediate linguistic context. We attempt to capture all these contributing elements in an Affect Calculus and demonstrate experimentally that the resulting method can accurately approximate human judgment. The work reported here is part of a larger effort to develop a highly accurate system for identifying, classifying, and comparing metaphors occurring in large volumes of text across four different languages: English, Spanish, Russian, and Farsi.

Modelling the interplay of metaphor and emotion through multitask learning

“…Existing work combining metaphor and emotion either focuses on the inclusion of emotion features in metaphor identification or on the automatic identification of affect carried by metaphors. Kozareva (2013) and Strzalkowski et al (2014) modelled the affect carried by metaphors and evaluate their approaches on a metaphor-rich corpus containing data from four languages. Kozareva (2013) performs polarity classification and valence regression using the AdaBoost classifier and support vector regression trained on information from the sentence, its context, and source and target domain annotations.…”

“…However, the two phenomena have been typically modelled independently. Exceptions include the use of hand-engineered emotion features when training a classifier for metaphor identification (Strzalkowski et al, 2014) and auto-matic identification of affect carried by metaphors (Kozareva, 2013;Strzalkowski et al, 2014). However, none of this research has attempted to model metaphor and emotion within a unified model of semantic composition.…”

Metaphor as a Medium for Emotion: An Empirical Study

“…A number of computational approaches for sentiment polarity classification of metaphorical language have also been proposed (Veale and Li, 2012;Kozareva, 2013;Strzalkowski et al, 2014). However, there is no quantitative study establishing the extent to which metaphorical language is used to express emotion nor a data-supported account of the mechanisms by which this happens.…”

Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions

“…The interaction between emotion and metaphor has been studied from different perspectives by scholars in many fields such as psychology (Averill, 1990;Thibodeau and Boroditsky, 2011;Fetterman et al, 2016), linguistics (Fainsilber and Ortony, 1987;Kövecses, 2010), neuroscience (Az-iz-Zadeh and Damasio, 2008;Malinowski and Horton, 2015;Jabbi et al, 2008) and natural language processing (NLP) (Mohammad, Shutova and Turney, 2016). Many approaches for sentiment analysis of metaphorical texts have been proposed in the area of NLP (Smith et al, 2007;Veale, 2012;Reyes and Rosso, 2012;Kozareva, 2013;Strzalkowski et al, 2014). In particular, along with the rapid explosion of social media applications such as Twitter and Weibo, emotional texts containing metaphorical expressions have increased considerably.…”