Modelling Argumentation for an User Opinion Aggregation Tool

Pablo Weingart, Thiemo Wambsganss, Matthias Soellner

Abstract
We introduce an argumentation annotation scheme that models basic argumentative structure and additional contextual details across diverse user opinion domains. Drawing from established argumentation modeling approaches and related theory on user opinions, the scheme integrates the concepts of argumentative components, specificity, sentiment and aspects of the user opinion domain. Our freely available dataset includes 1,016 user opinions with 7,266 sentences, spanning products from 19 e-commerce categories, restaurants, hotels, local services, and mobile applications. Utilizing the dataset, we trained three transformer-based models, demonstrating their efficacy in predicting the annotated classes for identifying argumentative statements and contextual details from user opinion documents. Finally, we evaluate a prototypical dashboard that integrates the model inferences to aggregate information and rank exemplary products based on a vast array of user opinions. Early results from an experimental evaluation with eighteen users include positive user perceptions but also highlight challenges when condensing detailed argumentative information to users.
Anthology ID:
2024.lrec-main.1009
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
11548–11559
Language:
URL:
https://aclanthology.org/2024.lrec-main.1009
DOI:
Bibkey:
Cite (ACL):
Pablo Weingart, Thiemo Wambsganss, and Matthias Soellner. 2024. Modelling Argumentation for an User Opinion Aggregation Tool. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11548–11559, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Modelling Argumentation for an User Opinion Aggregation Tool (Weingart et al., LREC-COLING 2024)
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