Analysis of user experience in low-resource languages: A case study of the Uzbek language Google Play reviews
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AARM3FCQB" target="_blank" >RIV/00216208:11320/26:ARM3FCQB - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1016/j.ipm.2024.104015" target="_blank" >http://dx.doi.org/10.1016/j.ipm.2024.104015</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ipm.2024.104015" target="_blank" >10.1016/j.ipm.2024.104015</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Analysis of user experience in low-resource languages: A case study of the Uzbek language Google Play reviews
Popis výsledku v původním jazyce
Understanding user experience is crucial for business success, yet analyzing user reviews in low-resource languages presents significant challenges due to the scarcity of annotated data. To address this gap, we conducted an in-depth analysis of 27,985 Uzbek reviews from the Google Play Store, focusing on the six key aspects of the User Experience Honeycomb model. Our study meticulously annotated these reviews, comprising a total of 43,712 sentences, to assess the sentiment polarity across these six dimensions. To benchmark this task, we propose an integrated framework that leverages pre-trained models along with GCN to capture semantic relationships, thereby enhancing the accuracy of sentiment analysis. Our approach demonstrated superior performance, achieving an absolute improvement of 0.30 in the F1 score for multi-classification tasks and 0.43 for binary classification tasks compared to existing baseline methods. These results underscore the effectiveness of our proposed framework in understanding user experience in low-resource language contexts, offering valuable insights for businesses and researchers alike. © 2024 Elsevier Ltd
Název v anglickém jazyce
Analysis of user experience in low-resource languages: A case study of the Uzbek language Google Play reviews
Popis výsledku anglicky
Understanding user experience is crucial for business success, yet analyzing user reviews in low-resource languages presents significant challenges due to the scarcity of annotated data. To address this gap, we conducted an in-depth analysis of 27,985 Uzbek reviews from the Google Play Store, focusing on the six key aspects of the User Experience Honeycomb model. Our study meticulously annotated these reviews, comprising a total of 43,712 sentences, to assess the sentiment polarity across these six dimensions. To benchmark this task, we propose an integrated framework that leverages pre-trained models along with GCN to capture semantic relationships, thereby enhancing the accuracy of sentiment analysis. Our approach demonstrated superior performance, achieving an absolute improvement of 0.30 in the F1 score for multi-classification tasks and 0.43 for binary classification tasks compared to existing baseline methods. These results underscore the effectiveness of our proposed framework in understanding user experience in low-resource language contexts, offering valuable insights for businesses and researchers alike. © 2024 Elsevier Ltd
Klasifikace
Druh
J<sub>SC</sub> - 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)
Návaznosti výsledku
Projekt
—
Návaznosti
—
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Information Processing and Management
ISSN
0306-4573
e-ISSN
—
Svazek periodika
62
Číslo periodika v rámci svazku
3
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
15
Strana od-do
104015
Kód UT WoS článku
—
EID výsledku v databázi Scopus
2-s2.0-85211156613