Engineering, Mathematics and Computer Science Journal (EMACS)
Vol. 7 No. 1 (2025): EMACS

Effectiveness Analysis of RoBERTa and DistilBERT in Emotion Classification Task on Social Media Text Data

Nabiilah, Ghinaa Zain (Unknown)



Article Info

Publish Date
31 Jan 2025

Abstract

The development of social media provides various benefits in various ways, especially in the dissemination of information and communication. Through social media, users can express their opinions, or even their feelings. In this regard, sometimes users also convey information or opinions according to the user's feelings or emotions. This triggers the impact of aggressive online behavior, including cyberbullying, which triggers unhealthy debates on social media. The development of deep learning models has also been developed in several ways, especially emotion classification. In addition to using deep learning models, the development of classification tasks has also been carried out using transformer architectures, such as BERT. The development of the BERT model continues to be carried out, so this study will analyze and explore the application of BERT model development, such as RoBERTa and DistilBERT. The optimal result of this study is with an accuracy value of 92.69% using the RoBERTa model.

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Journal Info

Abbrev

EMACS

Publisher

Subject

Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

Description

Engineering, MAthematics and Computer Science (EMACS) Journal invites academicians and professionals to write their ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food ...