Adequate Prompting Improves Performance of Regression Models of Emotional Content

Author:

Bulla Luana1ORCID,Mongiovì Misael1ORCID

Affiliation:

1. University of Catania, Italy and ISTC - National Research Council, Italy

Publisher

ACM

Reference34 articles.

1. Sven Buechel and Udo Hahn. 2016. Emotion analysis as a regression problem–dimensional models and their implications on emotion representation and metrical evaluation. In ECAI 2016. IOS Press, 1114–1122.

2. Sven Buechel and Udo Hahn. 2022. Emobank: Studying the impact of annotation perspective and representation format on dimensional emotion analysis. arXiv preprint arXiv:2205.01996 (2022).

3. Valence and Arousal-Infused Bi-Directional LSTM for Sentiment Analysis of Government Social Media Management

4. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116 (2019).

5. Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, and Sujith Ravi. 2020. GoEmotions: A dataset of fine-grained emotions. arXiv preprint arXiv:2005.00547 (2020).

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