Fine-tuning Fine-tuned Models: Towards a Practical Methodology for Sentiment Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10511634" target="_blank" >RIV/00216208:11320/25:10511634 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-981-96-7005-5_1" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-96-7005-5_1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-96-7005-5_1" target="_blank" >10.1007/978-981-96-7005-5_1</a>

Result language

angličtina

Original language name

Fine-tuning Fine-tuned Models: Towards a Practical Methodology for Sentiment Analysis

Original language description

Sentiment classifiers are typically built by annotating a relatively small data sample and fine-tuning a pre-trained language model. This approach overlooks the opportunity created by the emergence of open-source sentiment classifiers trained on large collections of supervised data from a variety of domains. These models often exhibit superior classification performance and can be used out-of-the-box, but still their performance may be negatively affected by the domain shift. This could potentially be eliminated by annotating in-domain data and further fine-tuning the model, but fine-tuning of the already fine-tuned models has not been investigated in the context of sentiment analysis and has often been unsuccessful for other NLP tasks. This paper presents an experimental analysis of this issue, studying the performance of three off-the-shelf sentiment classifiers fine-tuned using 14 different methods on customer reviews in three languages. The results show that fine-tuning of already fine-tuned model

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Neural Information Processing

ISBN

978-981-9670-04-8

ISSN

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e-ISSN

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Number of pages

16

Pages from-to

1-16

Publisher name

Springer

Place of publication

Singapore

Event location

Auckland, New Zealand

Event date

Dec 2, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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