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        <namePart type="given">Gabriel</namePart>
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    <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.</abstract>
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        <url>https://aclanthology.org/2022.lrec-1.62</url>
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%0 Conference Proceedings
%T Multi-source Multi-domain Sentiment Analysis with BERT-based Models
%A Roccabruna, Gabriel
%A Azzolin, Steve
%A Riccardi, Giuseppe
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F roccabruna-etal-2022-multi
%X 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.
%U https://aclanthology.org/2022.lrec-1.62
%P 581-589

Markdown (Informal)

[Multi-source Multi-domain Sentiment Analysis with BERT-based Models](https://aclanthology.org/2022.lrec-1.62) (Roccabruna et al., LREC 2022)

• Multi-source Multi-domain Sentiment Analysis with BERT-based Models (Roccabruna et al., LREC 2022)

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.