Dataset origin: https://zenodo.org/records/3756607

Description

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.

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 .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.

Citation

@inproceedings{buechel-etal-2020-learning-evaluating,
    title = "Learning and Evaluating Emotion Lexicons for 91 Languages",
    author = {Buechel, Sven  and
      R{\"u}cker, Susanna  and
      Hahn, Udo},
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.112/",
    doi = "10.18653/v1/2020.acl-main.112",
    pages = "1202--1217",
    abstract = "Emotion lexicons describe the affective meaning of words and thus constitute a centerpiece for advanced sentiment and emotion analysis. Yet, manually curated lexicons are only available for a handful of languages, leaving most languages of the world without such a precious resource for downstream applications. Even worse, their coverage is often limited both in terms of the lexical units they contain and the emotional variables they feature. In order to break this bottleneck, we here introduce a methodology for creating almost arbitrarily large emotion lexicons for any target language. Our approach requires nothing but a source language emotion lexicon, a bilingual word translation model, and a target language embedding model. Fulfilling these requirements for 91 languages, we are able to generate representationally rich high-coverage lexicons comprising eight emotional variables with more than 100k lexical entries each. We evaluated the automatically generated lexicons against human judgment from 26 datasets, spanning 12 typologically diverse languages, and found that our approach produces results in line with state-of-the-art monolingual approaches to lexicon creation and even surpasses human reliability for some languages and variables. Code and data are available at \url{https://github.com/JULIELab/MEmoLon} archived under DOI 10.5281/zenodo.3779901."
}