Cross-domain corpus selection for cold-start context

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Cross-domain corpus selection for cold-start context

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Cross-domain corpus selection for cold-start context

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Information Science

ISSN

0165-5515

e-ISSN

—

Svazek periodika

""

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85199769900