Analisis Sentimen untuk Ulasan Produk E-Commerce Shopee Menggunakan BERT
DOI:
https://doi.org/10.55601/jsm.v26i2.1796Keywords:
Analisis Sentimen, BERT, E-commerce, Opini KonsumenAbstract
Analisis sentimen sangat penting untuk memahami opini konsumen dan menyempurnakan strategi e-commerce. Analisis ini menghadapi tantangan seperti bahasa informal, ambiguitas semantik, dan inkonsistensi antara sentimen tekstual dan peringkat bintang, yang memengaruhi akurasi klasifikasi. Penelitian ini menerapkan model BERT (Bidirectional Encoder Representations from Transformersi) untuk mengklasifikasikan sentimen dalam ulasan pengguna Shopee. Data dikumpulkan dari penelitian sebelumnya dan menjalani praproses, termasuk tokenisasi, penghapusan stopword, dan normalisasi teks. Pendekatan analisis sentimen berbasis leksikon digunakan sebagai dasar perbandingan. Model BERT disempurnakan menggunakan optimasi hiperparameter, mencapai akurasi 83,08%, presisi 82,91%, recall 83,08%, dan F1-score 82,87%. Dibandingkan dengan studi sebelumnya yang menggunakan Naïve Bayes dengan N-Gram dan Information Gain, yang mencapai akurasi 92% tetapi presisi lebih rendah (56%), recall (65%), dan F1-score (60%), BERT mengungguli dengan metrik evaluasi yang lebih seimbang dan keandalan prediktif yang lebih besar. Hasil ini menunjukkan kemampuan BERT untuk menangkap konteks semantik dua arah, melampaui metode tradisional dalam menangani tugas analisis sentimen yang kompleks.
References
K. Selvasheela, A. M. Abirami, and A. K. Askarunisa, “Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering,” 2023, Tech Science Press. doi: 10.32604/csse.2023.029148.
A. S. Otto, D. M. Szymanski, and R. Varadarajan, “Customer satisfaction and firm performance: insights from over a quarter century of empirical research,” J Acad Mark Sci, vol. 48, no. 3, pp. 543–564, 2020, doi: 10.1007/s11747-019-00657-7.
F. M. Dewi, L. Sulivyo, and Listiawati, “Influence of Consumer Behavior and Marketing Mix on Product Purchasing Decisions,” APTISI Transactions on Management, vol. 6, no. 2, pp. 151–157, Feb. 2022, doi: 10.33050/atm.v6i2.1737.
R. Lourdusamy, P. Thangavel, and J. S, “Sentiments Unleashed : Pioneering the Frontier of Sentiment Analysis through Cutting-Edge Applications and Methodologies,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10, pp. 205–220, Feb. 2024, doi: 10.32628/CSEIT24105105.
Nikhil Sanjay Suryawanshi, “Sentiment analysis with machine learning and deep learning: A survey of techniques and applications,” International Journal of Science and Research Archive, vol. 12, no. 2, pp. 005–015, Jul. 2024, doi: 10.30574/ijsra.2024.12.2.1205.
M. Rizwan Rashid, A. Nawaz, A. Raza, A. Alahmadi, and T. Alsaedi, “Sentiment Mining in E-Commerce: The Transformer-based Deep Learning Model,” International journal of electrical and computer engineering systems, vol. 15, pp. 641–650, Feb. 2024, doi: 10.32985/ijeces.15.8.2.
pir bakhsh, M. I. Mangrio, M. khan, M. shaikh, and R. Memon, “Optimisation of Sentiment Analysis for E-Commerce,” VFAST Transactions on Software Engineering, vol. 12, pp. 243–262, Feb. 2024, doi: 10.21015/vtse.v12i3.1907.
Y. Liu, “Sentiment Analysis for E-commerce Product Reviews by Deep Learning Model of Bert- BiGRU-Softmax,” Journal of Corrosion and Materials, vol. 48, pp. 42–57, Feb. 2024, doi: 10.61336/jcm2023-5.
J. Saura, D. Palacios-Marqués, and D. Ribeiro-Soriano, “Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks,” Information Systems and e-Business Management, pp. 1–21, Feb. 2023, doi: 10.1007/s10257-023-00631-5.
M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowl Based Syst, vol. 226, p. 107134, 2021, doi: https://doi.org/10.1016/j.knosys.2021.107134.
P. Yadav, I. Kashyap, and B. S. Bhati, “Contextual Ambiguity Framework for Enhanced Sentiment Analysis,” Tehnicki Glasnik, vol. 18, no. 3, pp. 385–393, 2024, doi: 10.31803/tg-20231227064230.
M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artif Intell Rev, vol. 55, no. 7, pp. 5731–5780, 2022, doi: 10.1007/s10462-022-10144-1.
P. Dmytro and B. Oleh, “Review of methods for semantic text classification????? ??????? ??????????? ???????????? ??????,” System technologies, vol. 5, pp. 120–136, Feb. 2024, doi: 10.34185/1562-9945-5-154-2024-13.
M. Sal?c? and Ü. E. Ölçer, “Impact of Transformer-Based Models in NLP: An In-Depth Study on BERT and GPT,” in 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), 2024, pp. 1–6. doi: 10.1109/IDAP64064.2024.10710796.
F. M. Sinaga, R. Purba, S. J. Pipin, W. S. Lestari, and S. Winardi, “Optimization of Sentiment Analysis Classification of ChatGPT on Big Data Twitter in Indonesia using BERT,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 3, p. 1665, Jul. 2024, doi: 10.30865/mib.v8i3.7861.
A. Sunjaya, N. Wijaya, P. W. Ng, and S. Winardi, Implementation of Sentiment Analysis of Shopee E-Commerce Reviews using Naïve Bayes, N-Gram, and Information Gain. 2023. doi: 10.1109/ICIC60109.2023.10381959.
P. Shah, H. Patel, and P. Swaminarayan, “Multitask Sentiment Analysis and Topic Classification Using BERT,” ICST Transactions on Scalable Information Systems, vol. 11, Jul. 2024, doi: 10.4108/eetsis.5287.
K. Clark, U. Khandelwal, O. Levy, and C. D. Manning, “What Does BERT Look At? An Analysis of BERT’s Attention,” Jun. 2019, [Online]. Available: http://arxiv.org/abs/1906.04341
S. Ruder, M. E. Peters, S. Swayamdipta, and T. Wolf, “Transfer Learning in Natural Language Processing,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, A. Sarkar and M. Strube, Eds., Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 15–18. doi: 10.18653/v1/N19-5004.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Oct. 2018, [Online]. Available: http://arxiv.org/abs/1810.04805
A. Vaswani et al., “Attention Is All You Need,” Jun. 2017, [Online]. Available: http://arxiv.org/abs/1706.03762
T. Wolfe, “E-commerce,” Springer Handbooks, pp. 1411–1430, Jan. 2023, doi: 10.1007/978-3-030-96729-1_67.
A. A. Tanjung and M. E. Syafii, “Analisis Pengaruh Ekonomi Digital Terhadap Pertumbuhan Ekonomi di Indonesia: Model Data Panel,” Ekonomi, Keuangan, Investasi dan Syariah (EKUITAS), vol. 4, no. 2, pp. 567–575, Dec. 2022, doi: 10.47065/ekuitas.v4i2.2223.
H. Liao, “Learning consumer preferences from online textual reviews and ratings based on the aggregation-disaggregation paradigm with attitudinal Choquet integral,” Economic Research-Ekonomska Istraživanja, vol. 36, no. 2, Aug. 2022, doi: 10.1080/1331677x.2022.2106282.
“Sentiment Analysis Using Text Mining: A Survey,” International Research Journal of Modernization in Engineering Technology and Science, Aug. 2023, doi: 10.56726/irjmets43989.
L. M. Pham and H. L. The, “LNLF-BERT: Transformer for Long Document Classification with Multiple Attention Levels,” IEEE Access, p. 1, Jan. 2024, doi: 10.1109/access.2024.3492102.
S. Talebi, E. Tong, A. Li, G. Yamin, G. Zaharchuk, and M. Mofrad, “Exploring the performance and explainability of fine-tuned BERT models for neuroradiology protocol assignment,” BMC Med Inform Decis Mak, vol. 24, May 2024, doi: 10.1186/s12911-024-02444-z.
S. Daisy, F. Mary, and G. Mageswary, “An Efficient Data Preprocessing Techniques for Sentiment Analysis Using MOOC Dataset in Machine Learning,” Conference Proceeding Issue Published in International Journal of Trend in Research and Development.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
J. Hartmann, M. Heitmann, and C. Schamp, “More than a Feeling: Accuracy and Application of Sentiment Analysis Christian Siebert 2.” [Online]. Available: https://ssrn.com/abstract=3489963
H. Sharma, R. Ajmera, and D. Dharamdasani, “Statistical Analysis and Accuracy Assessment of Improved Machine Learning Based Opinion Mining Framework,” 2024. [Online]. Available: https://internationalpubls.com
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