Cross-domain corpus selection for cold-start context

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A2UJR98YL" target="_blank" >RIV/00216208:11320/25:2UJR98YL - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199769900&doi=10.1177%2f01655515241263283&partnerID=40&md5=69fe9ff2a06f514aef98c1be5d9bc919" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199769900&doi=10.1177%2f01655515241263283&partnerID=40&md5=69fe9ff2a06f514aef98c1be5d9bc919</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/01655515241263283" target="_blank" >10.1177/01655515241263283</a>

Result language

angličtina

Original language name

Cross-domain corpus selection for cold-start context

Original language description

Sentiment analysis is a powerful tool for monitoring attitudes towards companies, products or services and identifying specific features that drive positive or negative sentiment. However, collecting labelled data for training sentiment analysis models in a specific domain can be challenging in practical applications. One promising solution to this ‘cold-start’ problem is domain adaptation, which leverages labelled data from a related source domain to train a model for the target domain. A critical yet often neglected aspect in prior research is the measurement of similarity between the source and target domains, a factor that greatly impacts the success of domain adaptation. To fill this gap, we propose a novel measure that combines semantic, syntactic and lexical features to assess corpus-level similarity between two domains. Our experimental results demonstrate that our method achieves high precision (0.91) and recall (0.75), outperforming traditional methods. Moreover, our proposed measure can assist new domain products in selecting the most suitable training data set for their sentiment analysis tasks. © The Author(s) 2024.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

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

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Publication year

2024

Confidentiality

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

Name of the periodical

Journal of Information Science

ISSN

0165-5515

e-ISSN

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Volume of the periodical

""

Issue of the periodical within the volume

2024

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

1-18

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85199769900