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A:
Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons.
In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word–emotion and word–polarity association lexicon quickly and inexpensively.
We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them.
Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level).
We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion.
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K:
Emotions, affect, polarity, semantic orientation, crowdsourcing, Mechanical Turk, emotion lexicon, polarity lexicon, word–emotion associations, sentiment analysis.
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P:
https://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2012.00460.x
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D:
https://arxiv.org/pdf/1308.6297.pdf
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S:
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R:
https://www.researchgate.net/publication/256199465_Crowdsourcing_a_Word-Emotion_Association_Lexicon
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G:
https://app.razzi.my/findgref?gid=15LQdmmfNMwhK7jb-4zkPkKOjZ0apm6fz
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