Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States
Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, SeyedAbolghasem Mirroshandel, Owen Rambow
Abstract
Opinion mining is an important task in natural language processing. The MPQA Opinion Corpus is a fine-grained and comprehensive dataset of private states (i.e., the condition of a source who has an attitude which may be directed toward a target) based on context. Although this dataset was released years ago, because of its complex definition of annotations and hard-to-read data format, almost all existing research works have only focused on a small subset of the dataset. In this paper, we present a comprehensive study of the entire MPQA 2.0 dataset. In order to achieve this goal, we first provide a clean version of MPQA 2.0 in a more interpretable format. Then, we propose two novel approaches for opinion mining, establishing new high baselines for future work. We use two pre-trained large language models, BERT and T5, to automatically identify the type, polarity, and intensity of private states expressed in phrases, and we use T5 to detect opinion expressions and their agents (i.e., sources).- Anthology ID:
- 2024.lrec-main.1093
- 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:
- 12481–12495
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1093
- DOI:
- Cite (ACL):
- Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, SeyedAbolghasem Mirroshandel, and Owen Rambow. 2024. Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12481–12495, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States (Ahmadnia et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1093.pdf