Abstract
The problem of subjectivity detection is often approached as a preparatory binary task for sentiment analysis, despite the fact that theoretically subjectivity is often defined as a matter of degree. In this work, we approach subjectivity analysis as a regression task and test the efficiency of a transformer RoBERTa model in annotating subjectivity of online news, including news from social media, based on a small subset of human-labeled training data. The results of experiments comparing our model to an existing rule-based subjectivity regressor and a state-of-the-art binary classifier reveal that: 1) our model highly correlates with the human subjectivity ratings and outperforms the widely used rule-based “pattern” subjectivity regressor (De Smedt and Daelemans, 2012); 2) our model performs well as a binary classifier and generalizes to the benchmark subjectivity dataset (Pang and Lee, 2004); 3) in contrast, state-of-the-art classifiers trained on the benchmark dataset show catastrophic performance on our human-labeled data. The results bring to light the issues of the gold standard subjectivity dataset, and the models trained on it, which seem to distinguish between the origin/style of the texts rather than subjectivity as perceived by human English speakers.- Anthology ID:
- 2023.wassa-1.27
- Volume:
- Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Jeremy Barnes, Orphée De Clercq, Roman Klinger
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 305–314
- Language:
- URL:
- https://aclanthology.org/2023.wassa-1.27
- DOI:
- 10.18653/v1/2023.wassa-1.27
- Cite (ACL):
- Elena Savinova and Fermin Moscoso Del Prado. 2023. Analyzing Subjectivity Using a Transformer-Based Regressor Trained on Naïve Speakers’ Judgements. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 305–314, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Analyzing Subjectivity Using a Transformer-Based Regressor Trained on Naïve Speakers’ Judgements (Savinova & Moscoso Del Prado, WASSA 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.wassa-1.27.pdf