Abstract
This work applies Natural Language Processing (NLP) techniques, specifically transformer models, for the emotional evaluation of open-ended responses. Today’s powerful advances in transformer architecture, such as ChatGPT, make it possible to capture complex emotional patterns in language. The proposed transformer-based system identifies the emotional features of various texts. The research employs an innovative approach, using prompt engineering and existing context, to enhance the emotional expressiveness of the model. It also investigates spaCy’s capabilities for linguistic analysis and the synergy between transformer models and this technology. The results show a significant improvement in emotional detection compared to traditional methods and tools, highlighting the potential of transformer models in this domain. The method can be implemented in various areas, such as emotional research or mental health monitoring, creating a much richer and complete user profile.
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Notes
- 1.
Available at https://openai.com, reviewed in January 2024.
- 2.
Available at https://ai.google, reviewed in January 2024.
- 3.
Available at https://www.anthropic.com/, reviewed in January 2024.
- 4.
Available at https://www.anthropic.com/index/introducing-claude, reviewed in January 2024.
- 5.
Available at https://inflection.ai/about, reviewed in January 2024.
- 6.
Available at https://lmsys.org/blog/2023-03-30-vicuna/, reviewed in January 2024.
- 7.
A conversation comprises one or more interspersed interactions between speakers.
- 8.
Available at https://www.json.org/json-en.html, reviewed in January 2024.
- 9.
Word used to designate a set of input characters submitted to the model.
- 10.
Available at https://spacy.io, reviewed in January 2024.
- 11.
Available at https://spacy.io/universe/project/spacy-textblob reviewed in January 2024.
- 12.
Available at https://textblob.readthedocs.io/en/dev/, reviewed in January 2024.
- 13.
Available at https://www.kaggle.com/datasets/projjal1/human-conversation-training-data, reviewed in January 2024.
- 14.
Available at https://www.kaggle.com/datasets/miguelcorraljr/ted-ultimate-dataset, reviewed in January 2024.
- 15.
Available at https://huggingface.co/datasets/hita/social-behavior-emotions, reviewed in January 2024.
- 16.
The model hyperparameters were set to temperature=0.0, \({\small {\texttt {top}}}\_{\small {\texttt {p=1.0}}}\) (default value), \(\small {\texttt {frequency}}\_\small {\texttt {penalty=0.0}}\) (default value), \(\small {\texttt {presence}}\_\small {\texttt {penalty=0.0}}\) (default value) and \(\small {\texttt {stop}}\_\small {\texttt {sequence=None}}\) (default value).
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Acknowledgement
This work was partially supported by: (i) Xunta de Galicia grants ED481B-2021-118 and ED481B-2022-093, Spain; and (ii) Portuguese national funds through FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) – as part of project UIDB/50014/2020 (DOI: 10.54499/UIDP/50014/2020 | https://doi.org/10.54499/UIDP/50014/2020).
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Pajón-Sanmartín, A., de Arriba-Pérez, F., García-Méndez, S., Burguillo, J.C., Leal, F., Malheiro, B. (2024). Emotional Evaluation of Open-Ended Responses with Transformer Models. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-031-60215-3_3
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