Word Affect Intensities

Word Affect Intensities Saif M. Mohammad National Research Council Canada saif.mohammad@nrc-cnrc.gc.ca Abstract Words often convey affect—emotions, feelings, and attitudes. Further, different words can convey affect to various degrees (intensities). However, existing manually created lexicons for basic emotions (such as anger and fear) indicate only coarse categories of affect associ- ation (for example, associated with anger or not associated with anger). Automatic lexicons of affect provide fine degrees of association, but they tend not to be accurate as human-created lexicons. Here, for the first time, we present a manually created affect intensity lexicon with real-valued scores of intensity for four basic emotions: anger, fear, joy, and sadness. (We will subsequently add entries for more emotions such as disgust, anticipation, trust, and surprise.) We refer to this dataset as the NRC Affect Intensity Lexicon, or AIL for short. AIL has entries for close to 6,000 English words. We used a technique called best–worst scaling (BWS) to create the lexicon. BWS improves annotation consistency and obtains reliable fine-grained scores (split-half reliability > 0.91). We also compare the entries in AIL with the entries in the NRC VAD Lexicon, which has valence, arousal, and dominance (VAD) scores for 20K English words. We find that anger, fear, and sadness words, on average, have very similar VAD scores. However, sadness words tend to have slightly lower dominance scores than fear and anger words. The Affect Intensity Lexicon has applications in automatic emotion analysis in a number of domains such as commerce, education, intelligence, and public health. AIL is also useful in the building of natural language generation systems. Keywords: emotion intensity, emotion lexicon, emotion analysis, crowdsourcing, best–worst scaling, sentiment analysis 1. Introduction Words often convey affect—emotions, feelings, and atti- tudes. Some words have affect as a core part of their meaning. For example, dejected and wistful denotate some amount of sadness (and are thus associated with sadness). On the other hand, some words are associated with affect even though they do not denotate affect. For example, fail- ure and death describe concepts that are usually accom- panied by sadness and thus they connotate some amount of sadness. Lexicons of word–affect association have nu- merous applications, including: tracking brand and product perception, tracking support for issues and policies, track- ing public health and well-being, literary analysis, and de- veloping more natural dialogue systems. Past work on man- ually compiling affect lexicons has focused on denotative words (Wiebe et al., 2005; Francisco and Gerv´as, 2006; Strapparava and Valitutti, 2004). A notable exception to this is the NRC Emotion Lexicon, which includes words that are associated with (or connotate) an emotion (Moham- mad and Turney, 2013). Words can be associated with different intensities (or degrees) of an emotion. For example, most people will agree that the word outrage is associated with a greater de- gree of anger (or more anger) than the word irritate. How- ever, existing manually created affect lexicons for basic emotions such as anger and fear do not provide scores for the intensity of the emotion. …

Content truncated to 500 words