Emotion Detection Using Contextual Embeddings for Indonesian Product Review Texts on E-commerce Platform
Keywords:
BERT, Contextual Embeddings, E-commerce Platform, Emotion Detection, Product Review Texts
Abstract
The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.
References
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[2] Y. S. Mao, L. Y. Zhang, and Y. R. Li, “Finding product problems from online reviews based on BERT-CRF model,” 2019.
[3] P. Nandwani and R. Verma, “A review on sentiment analysis and emotion detection from text,” Dec. 01, 2021, Springer. doi: 10.1007/s13278-021-00776-6.
[4] A. D. P. Ariyanto, C. Fatichah, and D. Purwitasari, “Semantic Role Labeling for Information Extraction on Indonesian Texts: A Literature Review,” in 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), IEEE, Jul. 2023, pp. 119–124. doi: 10.1109/ISITIA59021.2023.10221008.
[5] A. D. P. Ariyanto, D. Purwitasari, and C. Fatichah, “A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data,” IEEE Access, vol. 12, no. April, pp. 57917–57946, 2024, doi: 10.1109/ACCESS.2024.3392370.
[6] A. D. P. Ariyanto, Chastine Fatichah, and Agus Zainal Arifin, “Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 5, pp. 992–1000, 2021, doi: 10.29207/resti.v5i5.3499.
[7] K. S. Nugroho, F. A. Bachtiar, and W. F. Mahmudy, “Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study,” Journal of Information Systems Engineering and Business Intelligence, vol. 8, no. 1, pp. 61–70, Apr. 2022, doi: 10.20473/jisebi.8.1.61-70.
[8] Muljono, A. S. Winarsih, and C. Supriyanto, “Evaluation of classification methods for Indonesian text emotion detection,” Proceedings - 2016 International Seminar on Application of Technology for Information and Communication, ISEMANTIC 2016, pp. 130–133, 2016, doi: 10.1109/ISEMANTIC.2016.7873824.
[9] L. Efrizoni, S. Defit, M. Tajuddin, and A. Anggrawan, “Komparasi Ekstraksi Fitur dalam Klasifikasi Teks Multilabel Menggunakan Algoritma Machine Learning,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 3, pp. 653–666, 2022, doi: 10.30812/matrik.v21i3.1851.
[10] A. D. P. Ariyanto, L. A, A. Z. A, M. Maryamah, R. W. S, and R. I, “Metode Pembobotan Kata Berbasis Cluster Untuk Perangkingan Dokumen Berbahasa Arab,” Techno.Com, vol. 20, no. 2, pp. 259–267, May 2021, doi: 10.33633/tc.v20i2.4357.
[11] Amelia Devi Putri Ariyanto, “Interrelation Detection Between Hadith Books Using Word Embedding and Ensemble Learning (Thesis Magister),” Institut Teknologi Sepuluh Nopember, 2022. [Online]. Available: http://repository.its.ac.id/id/eprint/92825
[12] R. Sutoyo, S. Achmad, A. Chowanda, E. W. Andangsari, and S. Isa, “PRDECT-ID: Indonesian product reviews dataset for emotions classification tasks,” Data Brief, 2022, doi: 10.17632/574v66hf2v.1.
[13] F. zahra El-Alami, S. Ouatik El Alaoui, and N. En Nahnahi, “Contextual semantic embeddings based on fine-tuned AraBERT model for Arabic text multi-class categorization,” Journal of King Saud University - Computer and Information Sciences, 2021, doi: 10.1016/j.jksuci.2021.02.005.
[14] Rizky Haqmanullah Pambudi, B. D. Setiawan, and Indriati, “Penerapan Algoritma C4 . 5 Untuk Memprediksi Nilai Kelulusan Siswa Sekolah Menengah Berdasarkan Faktor Eksternal,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 7, pp. 2637–2643, 2018, [Online]. Available: http://j-ptiik.ub.ac.id5
[15] S. Satriya, R. H. D., Santoso, E., & Sutrisno, “Implementasi Metode Ensemble K-Nearest Neighbor untuk Prediksi Nilai Tukar Rupiah Terhadap Dollar Amerika,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (JPTIIK) Universitas Brawijaya, vol. 2, no. 4, pp. 1718–1725, 2018.
[16] I. W. Saputro and B. W. Sari, “Uji Performa Algoritma Naïve Bayes untuk Prediksi Masa Studi Mahasiswa,” Creative Information Technology Journal, vol. 6, no. 1, p. 1, 2020, doi: 10.24076/citec.2019v6i1.178.
[17] M. F. Fibrianda and A. Bhawiyuga, “Analisis Perbandingan Akurasi Deteksi Serangan Pada Jaringan Komputer Dengan Metode Naïve Bayes Dan Support Vector Machine (SVM),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 9, pp. 3112–3123, 2018.
Published
2024-07-24
How to Cite
Ariyanto, A. D. P., Fari Katul Fikriah, & Arif Fitra Setyawan. (2024). Emotion Detection Using Contextual Embeddings for Indonesian Product Review Texts on E-commerce Platform. Pixel :Jurnal Ilmiah Komputer Grafis, 17(1), 179-185. https://doi.org/10.51903/pixel.v17i1.2010
Section
Articles
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