@inproceedings{ngo-etal-2022-studemo,
title = "{S}tud{E}mo: A Non-aggregated Review Dataset for Personalized Emotion Recognition",
author = "Ngo, Anh and
Candri, Agri and
Ferdinan, Teddy and
Kocon, Jan and
Korczynski, Wojciech",
editor = "Abercrombie, Gavin and
Basile, Valerio and
Tonelli, Sara and
Rieser, Verena and
Uma, Alexandra",
booktitle = "Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.nlperspectives-1.7",
pages = "46--55",
abstract = "Humans{'} emotional perception is subjective by nature, in which each individual could express different emotions regarding the same textual content. Existing datasets for emotion analysis commonly depend on a single ground truth per data sample, derived from majority voting or averaging the opinions of all annotators. In this paper, we introduce a new non-aggregated dataset, namely StudEmo, that contains 5,182 customer reviews, each annotated by 25 people with intensities of eight emotions from Plutchik{'}s model, extended with valence and arousal. We also propose three personalized models that use not only textual content but also the individual human perspective, providing the model with different approaches to learning human representations. The experiments were carried out as a multitask classification on two datasets: our StudEmo dataset and GoEmotions dataset, which contains 28 emotional categories. The proposed personalized methods significantly improve prediction results, especially for emotions that have low inter-annotator agreement.",
}
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%0 Conference Proceedings
%T StudEmo: A Non-aggregated Review Dataset for Personalized Emotion Recognition
%A Ngo, Anh
%A Candri, Agri
%A Ferdinan, Teddy
%A Kocon, Jan
%A Korczynski, Wojciech
%Y Abercrombie, Gavin
%Y Basile, Valerio
%Y Tonelli, Sara
%Y Rieser, Verena
%Y Uma, Alexandra
%S Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F ngo-etal-2022-studemo
%X Humans’ emotional perception is subjective by nature, in which each individual could express different emotions regarding the same textual content. Existing datasets for emotion analysis commonly depend on a single ground truth per data sample, derived from majority voting or averaging the opinions of all annotators. In this paper, we introduce a new non-aggregated dataset, namely StudEmo, that contains 5,182 customer reviews, each annotated by 25 people with intensities of eight emotions from Plutchik’s model, extended with valence and arousal. We also propose three personalized models that use not only textual content but also the individual human perspective, providing the model with different approaches to learning human representations. The experiments were carried out as a multitask classification on two datasets: our StudEmo dataset and GoEmotions dataset, which contains 28 emotional categories. The proposed personalized methods significantly improve prediction results, especially for emotions that have low inter-annotator agreement.
%U https://aclanthology.org/2022.nlperspectives-1.7
%P 46-55
Markdown (Informal)
[StudEmo: A Non-aggregated Review Dataset for Personalized Emotion Recognition](https://aclanthology.org/2022.nlperspectives-1.7) (Ngo et al., NLPerspectives 2022)
ACL