@article{depechemood++-gsi-article-2018,
author = "Araque, Oscar and Lorenzo Gatti and Jacopo Staiano and Marco Guerini",
abstract = "Several lexica for sentiment analysis have been developed and made available in the NLP community. While most of these come with word polarity annotations (e.g. positive/negative), attempts at building lexica for finer-grained emotion analysis (e.g. happiness, sadness) have recently attracted significant attention. Such lexica are often exploited as a building block in the process of developing learning models for which emotion recognition is needed, and/or used as baselines to which compare the performance of the models. In this work, we contribute two new resources to the community: a) an extension of an existing and widely used emotion lexicon for English; and b) a novel version of the lexicon targeting Italian. Furthermore, we show how simple techniques can be used, both in supervised and unsupervised experimental settings, to boost performances on datasets and tasks of varying degree of domain-specificity. ",
keywords = "emotion analysis;lexical resources;lexicons",
title = " {D}epeche{M}ood++: a {B}ilingual {E}motion {L}exicon {B}uilt {T}hrough {S}imple {Y}et {P}owerful {T}echniques",
url = "https://arxiv.org/abs/1810.03660",
year = "2018",
}