Analisis Sentimen Pengguna Aplikasi ChatGPT Berdasarkan Ulasan Google Play Store Menggunakan Metode TF-IDF dan Support Vector Machine (SVM)
Keywords:
Analisis Sentimen, ChatGPT, TF-IDF, Support Vector Machine, Google Play StoreAbstract
Perkembangan teknologi kecerdasan buatan generatif seperti ChatGPT telah meningkatkan penggunaan aplikasi berbasis Artificial Intelligence (AI) dalam berbagai bidang, seperti pendidikan, pekerjaan, dan pencarian informasi. Meningkatnya jumlah pengguna menyebabkan munculnya berbagai ulasan yang berisi opini, pengalaman, serta tingkat kepuasan pengguna terhadap aplikasi tersebut. Oleh karena itu, diperlukan analisis sentimen untuk mengetahui kecenderungan opini pengguna berdasarkan ulasan yang diberikan. Penelitian ini bertujuan untuk membangun sistem analisis sentimen terhadap ulasan pengguna aplikasi ChatGPT menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF) sebagai metode ekstraksi fitur dan Support Vector Machine (SVM) sebagai metode klasifikasi. Dataset yang digunakan berupa 5.000 ulasan pengguna aplikasi ChatGPT yang diperoleh dari Google Play Store. Tahapan penelitian meliputi pengumpulan dataset, pelabelan sentimen berdasarkan rating pengguna, preprocessing teks, pembobotan fitur menggunakan TF-IDF, klasifikasi menggunakan algoritma SVM, serta evaluasi model menggunakan Accuracy, Precision, Recall, dan F1-Score. Hasil pengujian menunjukkan bahwa kombinasi metode TF-IDF dan SVM menghasilkan nilai Accuracy sebesar 90,10%, Precision sebesar 87,19%, Recall sebesar 90,10%, dan F1-Score sebesar 88,38%. Sistem yang dikembangkan berhasil mengklasifikasikan ulasan pengguna ke dalam tiga kategori sentimen, yaitu positif, negatif, dan netral, sehingga dapat membantu memahami persepsi pengguna terhadap aplikasi ChatGPT berdasarkan ulasan yang diberikan.
References
[1] Z. Wang, Q. Xie, Z. Xu, Y. Zhang, Y. Yu, and R. Fu, “Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study,” arXiv preprint arXiv:2304.04339, 2023.
[2] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment Classification using Machine Learning Techniques,” in Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, pp. 79–86, 2002.
[3] G. Salton and C. Buckley, “Term-weighting Approaches in Automatic Text Retrieval,” Information Processing & Management, vol. 24, no. 5, pp. 513–523, 1988.
[4] C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge, United Kingdom: Cambridge University Press, 2008.
[5] T. Joachims, “Text Categorization with Support Vector Machines: Learning with Many Relevant Features,” in Proceedings of the 10th European Conference on Machine Learning (ECML), pp. 137–142, 1998.
[6] F. Sebastiani, “Machine Learning in Automated Text Categorization,” ACM Computing Surveys, vol. 34, no. 1, pp. 1–47, Mar. 2002.
[7] A. Pak and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC), pp. 1320–1326, 2010.
[8] M. Hu and B. Liu, “Mining and Summarizing Customer Reviews,” in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177, 2004.
[9] T. Fawcett, “An Introduction to ROC Analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, Jun. 2006.
[10] M. Sokolova and G. Lapalme, “A Systematic Analysis of Performance Measures for Classification Tasks,” Information Processing & Management, vol. 45, no. 4, pp. 427–437, Jul. 2009.
[11] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002.
[12] Y. Kim, “Convolutional Neural Networks for Sentence Classification,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751, 2014.
[13] K. K. Chaturvedi, A. K. Gupta, and P. Prasad, Sentiment Analysis of ChatGPT User Reviews Using Machine Learning Approaches,”`International Journal of Information Management Data Insights, vol. 4, 2024.
[14] M. A. Aljanabi, M. Ghazi, A. H. Ali, and S. A. Abed, “ChatGPT: Open Possibilities, Challenges, and Implications in Education,” Journal of Artificial Intelligence and Applications, vol. 3, no. 1, pp. 1–10, 2023.
[15] A. A. Alsaeedi and M. K. Khan,“A Study on Sentiment Analysis Techniques of Social Media Data,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 2, pp. 1–10, 2021.
[16] R. K. Kaliyar, A. Goswami, and P. Narang, “FakeBERT: Fake News Detection in Social Media with a BERT Based Deep Learning Approach,” Multimedia Tools and Applications, vol. 80, pp. 11765–11788, 2021.
