Pemanfaatan Machine learning untuk Klasifikasi Sentimen Pelanggan pada Media Sosial
Keywords:
analisis sentimen, machine learning, media sosial, naive bayes, support vector machineAbstract
Penelitian ini bertujuan untuk menganalisis performa algoritma Naïve Bayes, Support Vector Machine (SVM), dan Long Short-Term Memory (LSTM) dalam klasifikasi sentimen pelanggan berbasis data media sosial. Dataset berupa 10.000 tweet dikumpulkan menggunakan Twitter API dengan kata kunci terkait opini pelanggan. Proses pra-pemrosesan data meliputi pembersihan teks, tokenisasi, Stemming, dan penghapusan Stop Words. Dataset dibagi menjadi training set (70%), validation set (15%), dan test set (15%). Hasil penelitian menunjukkan bahwa LSTM unggul dengan akurasi 91,2%, diikuti oleh SVM (84,7%) dan Naïve Bayes (78,5%). LSTM berhasil memahami pola semantik dan temporal dalam teks, menjadikannya algoritma terbaik untuk data media sosial yang kompleks. SVM memberikan hasil yang stabil dengan memanfaatkan fitur TF-IDF, sementara Naïve Bayes cocok untuk dataset kecil namun memiliki keterbatasan dalam memahami hubungan antar kata. Penelitian ini menunjukkan bahwa algoritma Machine learning dapat digunakan secara efektif untuk analisis sentimen pelanggan. Hasil penelitian ini diharapkan dapat menjadi panduan dalam memilih algoritma yang sesuai untuk strategi pemasaran digital berbasis data pelanggan.
Downloads
References
B. Wang and Y. Wang, “Sentiment analysis in social networks: Methods and applications. Amsterdam: Elsevier,” 2016.
B. Liu, “Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies,” vol. 5(1), pp. 1–167, 2012.
B. Pang and L. Lee, “Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval,” vol. 2(1-2), pp. 1–135, 2008.
E. Cambria, B. Schuller, Y. Xia, and C. Havasi, “New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems,” vol. 28(2), pp. 15–21, 2013.
M. Naufal, “Implementasi machine learning dalam klasifikasi data teks. Jakarta: Universitas Teknologi Indonesia.,” 2019.
S. M. Sebastian and A. Himawan, “Machine learning-based sentiment analysis: A review and application to customer feedback. Journal of Data Science Applications,” vol. 5(3), pp. 44–56, 2021.
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies,” pp. 142–150, 2011.
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space. Proceedings of the International Conference on Learning Representations (ICLR).,” 2013.
Y. Goldberg and O. Levy, “Word2Vec explained: Deriving Mikolov et al.’s negative-sampling word-embedding method,” vol. 1402.3722, 2014.
S. Zhang, H. Wang, and J. Liu, “Deep learning for sentiment analysis: A survey. New York: Springer,” 2020.
S. Hochreiter and J. Schmidhuber, “Long short-term memory. Neural Computation,” vol. 9(8), pp. 1735–1780, 1997.
B. Siswoyo, Z. A. Abas, A. N. C. Pee, R. Komalasari, and N. Suyatna, “Ensemble machine learning algorithm optimization of bankruptcy prediction of bank,” IAES International Journal of Artificial Intelligence, vol. 11, no. 2, pp. 679–686, Jun. 2022, doi: 10.11591/ijai.v11.i2.pp679-686.
T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,” pp. 785–794, 2016.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors. Nature,” vol. 323(6088), pp. 533–536.
D. Tang, B. Qin, and T. Liu, “Document modeling with gated recurrent neural network for sentiment classification. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics,” pp. 1422–1432, 2015.
