LUCE: A Dynamic Framework and Interactive Dashboard for Opinionated Text Analysis
Omnia Zayed, Gaurav Negi, Sampritha Hassan Manjunath, Devishree Pillai, Paul Buitelaar
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.
BibTeX
@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"
}