StudEmo - corpus of consumer reviews annotated with emotions

  Authors

Ngo, Anh ; Candri, Argi ; Ferdinan, Teddy ; Kocoń, Jan ; Korczyński, Wojciech

 Project URL

https://github.com/CLARIN-PL/personalized-nlp/tree/nlperspectives

 Date issued

2022-05-20

 Type

corpus

 Size

5182 texts

 Language(s)

English

 Description

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. 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.

 Publisher

Wrocław University of Science and Technology

 Subject(s)

emotion recognition personalization non-aggregated dataset learning human representation

 Collection(s)

CLARIN-PL

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