Papers by Yong-Yeol Ahn

3 papers
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment (P18-1)

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Challenge: SemAxis characterizes word semantics using many semantic axes in word-vector spaces beyond sentiment . lexicon-based text analysis assumes that meaning of words does not change across contexts . but, recent advances in vector-space representations can tackle this challenge .
Approach: They propose a framework to characterize word semantics using many semantic axes beyond sentiment . they demonstrate that SemAxis can capture nuanced semantic representations in multiple online communities .
Outcome: The proposed framework outperforms state-of-the-art approaches in building domain-specific sentiment lexicons.
Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition‐Informed Approach to Quantifying Identity Fusion from Text (2025.emnlp-main)

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Challenge: Existing methods for measuring identity fusion are limited and require controlled surveys or direct field contact.
Approach: They propose a new metric that integrates cognitive linguistics with large language models to measure identity fusion.
Outcome: The proposed metric outperforms existing methods and human annotations in violence risk assessment.
Predicting Anti-Asian Hateful Users on Twitter during COVID-19 (2021.findings-emnlp)

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Challenge: Xenophobia and polarization have accompanied widespread social media usage in many nations, attracting many researchers.
Approach: They apply natural language processing techniques to characterize Twitter users who began to post anti-Asian hate messages during COVID-19.
Outcome: The results show that it is possible to predict who later posted anti-Asian slurs on Twitter and Reddit.

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