Abstract
Sentiment analysis is one of the most widely studied tasks in natural language processing. While BERT-based models have achieved state-of-the-art results in this task, little attention has been given to its performance variability across class labels, multi-source and multi-domain corpora. In this paper, we present an improved state-of-the-art and comparatively evaluate BERT-based models for sentiment analysis on Italian corpora. The proposed model is evaluated over eight sentiment analysis corpora from different domains (social media, finance, e-commerce, health, travel) and sources (Twitter, YouTube, Facebook, Amazon, Tripadvisor, Opera and Personal Healthcare Agent) on the prediction of positive, negative and neutral classes. Our findings suggest that BERT-based models are confident in predicting positive and negative examples but not as much with neutral examples. We release the sentiment analysis model as well as a newly financial domain sentiment corpus.- Anthology ID:
- 2022.lrec-1.62
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 581–589
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.62
- DOI:
- Cite (ACL):
- Gabriel Roccabruna, Steve Azzolin, and Giuseppe Riccardi. 2022. Multi-source Multi-domain Sentiment Analysis with BERT-based Models. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 581–589, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Multi-source Multi-domain Sentiment Analysis with BERT-based Models (Roccabruna et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.lrec-1.62.pdf
- Code
- sislab/multi-source-multi-domain-sentiment-analysis-with-bert-based-models
- Data
- IMDb Movie Reviews