LUCE: a dynamic framework and interactive dashboard for opinionated text analysis

Zayed, Omnia, Negi, Gaurav, Manjunath, Sampritha Hassan , Pillai, Devishree, & Buitelaar, Paul. (2025). LUCE: a dynamic framework and interactive dashboard for opinionated text analysis. Paper presented at the 31st International Conference on Computational Linguistics: System Demonstrations, Abu Dhabi, UAE, January https://aclanthology.org/2025.coling-demos.11/

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.

Funder

Research Ireland
Taighde Éireann – Research Ireland
European Union’s Horizon 2020

Publisher

Association for Computational Linguistics

Rights

CC BY NC-SA

cbn

Collections

Data Science Institute (Conference Papers)