The results show that the transformer-based performs well compared to the traditional baseline model with the macro F1 score of 0.3738, highlighting the importance of robust multi-class social media political text classification.
Findings demonstrate that the deep transformer approach is far more robust in extracting semantics from informal Indonesian social media text, with practical implications for public policy decision-making.
S. Pradana, Etika Kartikadarma· JOURNAL OF APPLIED INFORMATI...· 0 citations
This study aims to analyze public sentiment toward the LPDP alumni controversy on social media using a deep learning approach. The research data consist of YouTube user comments related to the LPDP issue, which were processed through text preprocessing and automatically labeled using IndoBERT into three sentiment class...
Dwi Erzalianti, Joice Junansi Tandirerung, C. Suhaeni et al.· JOURNAL OF APPLIED INFORMATI...· 0 citations
This study successfully proposes a Long Short-Term Memory (LSTM)-based model for automatic classification of Indonesian regional song lyrics by language, demonstrating that LSTM effectively captures sequential linguistic patterns and contextual relationships within regional languages.
Muhammad Rizky, Anandita Priatama, Aviv Yuniar Rahman et al.· Buana Information Technology...· 0 citations
A hybrid approach is proposed that leverages frozen embeddings from the pre-trained language model cardiffnlp/twitter-roberta-base-offensive as a high-level semantic feature extractor, combined with the CatBoost gradient boosting algorithm as the final classifier, avoiding the computational cost associated with fine-tu...
Muhammad Elfarizi, Surya Agustian, Fitra Kurnia et al.· 0 citations
Recommendations are given for choosing a vectorization method depending on the data volume and system performance requirements in real time, and classical methods with linear models demonstrate competitive quality with significantly lower computational costs on small datasets.
O. I. Zakharova, S. Bednyak, Yaroslav Dmitrievich Kanunnikov· Infokommunikacionnye tehnolo...· 0 citations
Comparing and analysing the performance of several machine learning algorithms on fine-grained sentiment classification problems to examine their suitability and shortcomings for use as models in sentiment analysis suggests large language models perform significantly worse on the 28-class classification task in zero-sh...
Shangjiafeng Guo· International journal of eng...· 0 citations
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