Multi-source Multi-domain Sentiment Analysis with BERT-based Models

Gabriel Roccabruna, Steve Azzolin, Giuseppe Riccardi


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:
Bibkey:
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)
Copy Citation:
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