@inproceedings{badaro-etal-2018-emowordnet,
title = "{E}mo{W}ord{N}et: Automatic Expansion of Emotion Lexicon Using {E}nglish {W}ord{N}et",
author = "Badaro, Gilbert and
Jundi, Hussein and
Hajj, Hazem and
El-Hajj, Wassim",
booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-2009",
doi = "10.18653/v1/S18-2009",
pages = "86--93",
abstract = "Nowadays, social media have become a platform where people can easily express their opinions and emotions about any topic such as politics, movies, music, electronic products and many others. On the other hand, politicians, companies, and businesses are interested in analyzing automatically people{'}s opinions and emotions. In the last decade, a lot of efforts has been put into extracting sentiment polarity from texts. Recently, the focus has expanded to also cover emotion recognition from texts. In this work, we expand an existing emotion lexicon, DepecheMood, by leveraging semantic knowledge from English WordNet (EWN). We create an expanded lexicon, EmoWordNet, consisting of 67K terms aligned with EWN, almost 1.8 times the size of DepecheMood. We also evaluate EmoWordNet in an emotion recognition task using SemEval 2007 news headlines dataset and we achieve an improvement compared to the use of DepecheMood. EmoWordNet is publicly available to speed up research in the field on \url{http://oma-project.com}.",
}
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%0 Conference Proceedings
%T EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet
%A Badaro, Gilbert
%A Jundi, Hussein
%A Hajj, Hazem
%A El-Hajj, Wassim
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 jun
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F badaro-etal-2018-emowordnet
%X Nowadays, social media have become a platform where people can easily express their opinions and emotions about any topic such as politics, movies, music, electronic products and many others. On the other hand, politicians, companies, and businesses are interested in analyzing automatically people’s opinions and emotions. In the last decade, a lot of efforts has been put into extracting sentiment polarity from texts. Recently, the focus has expanded to also cover emotion recognition from texts. In this work, we expand an existing emotion lexicon, DepecheMood, by leveraging semantic knowledge from English WordNet (EWN). We create an expanded lexicon, EmoWordNet, consisting of 67K terms aligned with EWN, almost 1.8 times the size of DepecheMood. We also evaluate EmoWordNet in an emotion recognition task using SemEval 2007 news headlines dataset and we achieve an improvement compared to the use of DepecheMood. EmoWordNet is publicly available to speed up research in the field on http://oma-project.com.
%R 10.18653/v1/S18-2009
%U https://aclanthology.org/S18-2009
%U https://doi.org/10.18653/v1/S18-2009
%P 86-93
Markdown (Informal)
[EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet](https://aclanthology.org/S18-2009) (Badaro et al., SemEval 2018)
ACL