Overview of the ensemble PLMs-based model fine-tuning strategy for Sentiment Analysis. The XGBoost ensemble model is utilised for the combination of the PLM feature classification.

Overview of the ensemble PLMs-based model fine-tuning strategy for Sentiment Analysis. The XGBoost ensemble model is utilised for the combination of the PLM feature classification.

<p>Overview of the ensemble PLMs-based model fine-tuning strategy for Sentiment Analysis. The XGBoost ensemble model is utilised for the combination of the PLM feature classification.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Koena Ronny Mabokela (21492394) (author)
مؤلفون آخرون:	Mpho Primus (21492397) (author), Turgay Celik (14378019) (author)
منشور في:	2025
الموضوعات:	Ecology Sociology Immunology Science Policy Biological Sciences not elsewhere classified population without access leverage existing plms twitter sentiment dataset african languages poses advancing sentiment analysis trained language models div >< p related languages achieved existing multilingual plms sentiment analysis related languages labelled dataset multilingual plms multilingual pre developing plms resource languages native languages world ’ work expands tswana ), task adaptation specific task significant portion setswana </ sesotho </ sepedi </ safrisenti </ promising approach paper explores large data isizulu </ isixhosa </ gap leaves findings demonstrate avoiding training alternative solution 63 %.
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الوصف
الملخص:	<p>Overview of the ensemble PLMs-based model fine-tuning strategy for Sentiment Analysis. The XGBoost ensemble model is utilised for the combination of the PLM feature classification.</p>

Cannot write session to /tmp/vufind_sessions/sess_ib03d97bb7lvufdrqsd3f339lq