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Overview This deposit contains the resulting lexicons from our ACL 2020 paper "Learning and Evaluating Emotion Lexicons for 91 Languages". The main repository for this project – including models, experimental code, and analyses – can be found on GitHub or the associated zenodo deposit. Content This deposit includes four zip files, each one representing different versions of the lexicons. The one which we mainly refer to in the paper is MTL_grouped.zip. The other versions were employed as a baseline comparison (`ridge.zip`) or in a development experiment (all but `ridge.zip`). Each zip file contains 91 tsv files which are named <iso language code>.tsv. Please refer to the file lexicons_overview.csv to find the right code for your language. Each tsv file constitutes a large-scale emotion lexicon for a particular language, covering roughly between 100k and 2M word type entries. Each word is described in terms of eight emotional variables: valence, arousal, dominance, joy anger, sadness, fear, and disgust. Please refer to our paper (see below) for additional details. Citation If you use our emotion lexicons in your work, please cite our paper: (coming up soon).
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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