CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON
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Abstract
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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The record
- Venue
- Computational Intelligence
- Topic
- Sentiment Analysis and Opinion Mining
- Field
- Computer Science
- Canadian institutions
- National Research Council Canada
- Funders
- National Research Council Canada
- Keywords
- LexiconCrowdsourcingComputer scienceAnnotationWord (group theory)Natural language processingPolarity (international relations)Term (time)Sentiment analysisAssociation (psychology)Artificial intelligenceQuality (philosophy)PsychologyLinguisticsWorld Wide Web
- Has abstract in OpenAlex
- yes