@inproceedings{zayed-etal-2025-luce,
title = "{LUCE}: A Dynamic Framework and Interactive Dashboard for Opinionated Text Analysis",
author = "Zayed, Omnia and
Negi, Gaurav and
Manjunath, Sampritha Hassan and
Pillai, Devishree and
Buitelaar, Paul",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Mather, Brodie and
Dras, Mark",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-demos.11/",
pages = "104--116",
abstract = "We introduce LUCE, an advanced dynamic framework with an interactive dashboard for analysing opinionated text aiming to understand people-centred communication. The framework features computational modules of text classification and extraction explicitly designed for analysing different elements of opinions, e.g., sentiment/emotion, suggestion, figurative language, hate/toxic speech, and topics. We designed the framework using a modular architecture, allowing scalability and extensibility with the aim of supporting other NLP tasks in subsequent versions. LUCE comprises trained models, python-based APIs, and a user-friendly dashboard, ensuring an intuitive user experience. LUCE has been validated in a relevant environment, and its capabilities and performance have been demonstrated through initial prototypes and pilot studies."
}
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%0 Conference Proceedings
%T LUCE: A Dynamic Framework and Interactive Dashboard for Opinionated Text Analysis
%A Zayed, Omnia
%A Negi, Gaurav
%A Manjunath, Sampritha Hassan
%A Pillai, Devishree
%A Buitelaar, Paul
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Mather, Brodie
%Y Dras, Mark
%S Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zayed-etal-2025-luce
%X We introduce LUCE, an advanced dynamic framework with an interactive dashboard for analysing opinionated text aiming to understand people-centred communication. The framework features computational modules of text classification and extraction explicitly designed for analysing different elements of opinions, e.g., sentiment/emotion, suggestion, figurative language, hate/toxic speech, and topics. We designed the framework using a modular architecture, allowing scalability and extensibility with the aim of supporting other NLP tasks in subsequent versions. LUCE comprises trained models, python-based APIs, and a user-friendly dashboard, ensuring an intuitive user experience. LUCE has been validated in a relevant environment, and its capabilities and performance have been demonstrated through initial prototypes and pilot studies.
%U https://aclanthology.org/2025.coling-demos.11/
%P 104-116
Markdown (Informal)
[LUCE: A Dynamic Framework and Interactive Dashboard for Opinionated Text Analysis](https://aclanthology.org/2025.coling-demos.11/) (Zayed et al., COLING 2025)
ACL