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All Journal Natural Science: Journal of Science and Technology Jurnal Ecosolum Jurnal Nalar Pendidikan Medula Dinamika Ilmu JOURNAL OF APPLIED INFORMATICS AND COMPUTING AKURASI: Jurnal Riset Akuntansi dan Keuangan Abdimas Galuh : Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas Pariwisata DINAMIKA ILMU: Jurnal Pendidikan International Journal of Economics (IJEC) Jurnal Agrosilvopasture-Tech Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications Akurasi
Muhammad Yusran
Department of Ophthalmology, Faculty of Medicine, Universitas Lampung
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Evaluating Fasttext and Glove Embeddings for Sentiment Analysis of AI-Generated Ghibli-Style Images Sentana Putra, I Gusti Ngurah; Yusran, Muhammad; Sari, Jefita Resti; Suhaeni, Cici; Sartono, Bagus; Dito, Gerry Alfa
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

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Abstract

The development of text-to-image generation technology based on artificial intelligence has triggered mixed public reactions, especially when applied to iconic visual styles such as Studio Ghibli. This research aims to evaluate public sentiment towards the phenomenon of Ghibli-style AI images by comparing two static word embedding methods, namely FastText and GloVe, on three classification algorithms: Logistic Regression, Random Forest, and Convolutional Neural Network (CNN). Data in the form of Indonesian tweets were collected from Twitter using hashtags such as #ghibli, #ghiblistyle, and #hayaomiyazaki during the period 25 March to 25 April 2025. Each tweet was manually labelled with positive or negative sentiment, then preprocessed and represented using pre-trained FastText and GloVe embeddings. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics, both macro and weighted. Results showed that FastText consistently performed the best on most models, especially in terms of precision and overall accuracy, thanks to its ability to handle sub-word information and spelling variations in social media texts. The combination of CNN with FastText yielded the highest performance with a macro F1-score of 76.56% and accuracy of 84.69%. However, GloVe still showed competitive performance in recall on the Logistic Regression model, making it relevant for contexts that prioritise sentiment detection coverage. This study emphasizes the importance of selecting embeddings and models that are appropriate to the characteristics of the data and the purpose of the analysis in informal social media-based sentiment classification.
Co-Authors Ab., Ahmad AGUS Agus Mohamad Soleh Ahmad Khumaidi Alfa Dito, Gerry Alfred Trisakti Amos, Erpianto Andi Hasbi Bagus Sartono Bahrul Ulum Ilham, Bahrul Ulum Dalillah, Fathian Nur Damanik, Inta P N Dito, Gerry Alfa Erwin Erwin Fahira, Fani Hijrah Amaliah Azis Ilyas, Herlina Intanri Kurniati JUMARDI Kamila, Sabrina Adnin Kusman Sadik Mahyuddin Barni, Mahyuddin Maryana, Dwi Muhammad Sulaiman Zubair Munawir Mustam, Mariaulfa Nurlina Ibrahim Poke, Andi Alif Rahmatullah Ramdani, Nurfika Ramdanil Ramdanil Rengganis Wardani, Dyah Wulan Sumekar Safira, Dela Sahusilawane, Aphrodite M Sari, Jefita Resti Sentana Putra, I Gusti Ngurah Sitti Hadijah, Sitti Suhaeni, Cici Syariful Anam Wahab, Nurhikmah Zaman , Nur