Comparative Study of BiLSTM, RoBERTa, and DistilGPT-2 Models in Sentiment Analysis on YouTube Comments - ​​Willas Daniel Rorrong Lumban Tobing; Theresia Ratih Dewi Saputri (Komunitas Dosen Indonesia, 2026)
The proliferation of social media has transformed the communication landscape and positioned platforms like YouTube as vital repositories of public sentiment. Manual analysis is no longer feasible because of the exponential volume of user-generated content making automated solutions critical. This study addresses this challenge by evaluating the efficacy of three deep learning architectures including BiLSTM, RoBERTa, and DistilGPT-2 for multi-class sentiment classification, contributing a novel empirical comparison between recurrent, encoder, and generative models on noisy text. The research utilizes the