MULTI-ASPECT SENTIMENT ANALYSIS TERHADAP NVIDIA RTX DENGAN SUPPORT VECTOR MACHINE DAN WORD EMBEDDINGS
Abstract
Beberapa bulan belakangan ini, tren meningkatnya pembelian GPU terutama seri RTX kian meningkat, banyak orang berlomba lomba untuk membuat PC untuk meningkatkan kinerja dengan kondisi WFH(Work From Home) selama terjadinya pandemi. Peningkatan terhadap penggunaan GPU seri RTX ini tentunya mengundang pendapat dari berbagai macam kalangan. Hal ini menciptakan pro dan kontra di kalangan PC enthusiast, terutama di beberapa platform Media Sosial. Dari munculnya pro dan kontra ini, dapat dilakukan ekstraksi emosi terhadap komentar yang mengandung pro dan kontra tersebut. Metode ini disebut juga dengan Analisis Sentimen, tahapan yang perlu dilakukan untuk melakukan Analisis Sentimen ini yang pertama adalah preprocessing yang terdiri dari Noise Removal, Tokenizing, Stopword Removal. Kemudian dilanjukan dengan proses yang kedua, yaitu Ekstraksi Fitur dengan Word Embeddings dan Support Vector Machine. Berdasarkan Analisis Performansi pada model yang telah dibuat, menunjukkan bahwa pada penelitian ini SVM kernel Linear menunjukkan hasil yang terbaik pada seluruh dataset, sedangkan kernel rbf pada penelitian ini menunjukkan hasil yang kurang memuaskan sekalipun telah melakukan perubahan parameter pada Gamma dan C. Penelitian ini dibuat bertujuan untuk menganalisa performansi model dalam mengklasifikasikan sentimen sebuah teks dengan model SVM.
Kata kunci: support vector machine, preprocessing, word embeddings
References
S. Arafin Mahtab, N. Islam, and M. Mahfuzur Rahaman, “Sentiment analysis on Bangladesh cricket with support vector machine,” in 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), 2018.
D. A. Kristiyanti, A. H. Umam, M. Wahyudi, R. Amin, and L. Marlinda, “Comparison of SVM & naïve Bayes algorithm for sentiment analysis toward west java governor candidate period 2018-2023 based on public opinion on twitter,” in 2018 6th International Conference on Cyber and IT Service Management (CITSM), 2018.
D. A. Pisner, “Machine Learning || Support vector machine,” pp. 101–121, 2020.
M. Habib, M. Faris, A. Alomari, and H. Faris, “AltibbiVec: A word embedding model for medical and health applications in the Arabic language,” IEEE Access, vol. 9, pp. 133875–133888, 2021.
B. Wang, A. Wang, F. Chen, Y. Wang, and C.-C. J. Kuo, “Evaluating word embedding models: Methods and experimental results,” arXiv [cs.CL], 2019.
Y. Li and T. Yang, “Word embedding for understanding natural language: A survey,” in Studies in Big Data, Cham: Springer International Publishing, 2018, pp. 83–104.
M. Hu et al., “CAN: Constrained attention networks for multi-aspect sentiment analysis,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019.
Q. Jiang, L. Chen, R. Xu, X. Ao, and M. Yang, “A challenge dataset and effective models for aspect-based sentiment analysis,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019.
N. N. Kham, Ed., Lexicon Based Emotion Analysis on Twitter Data. 2019.
J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189–215, 2020.
M. Ahmad, S. Aftab, and I. Ali, “Sentiment Analysis of Tweets using SVM,” Int. J. Comput. Appl., vol. 177, no. 5, pp. 25–29, 2017.
J. F. Sánchez-Rada and C. A. Iglesias, “Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison,” Inf. Fusion, vol. 52, pp. 344–356, 2019.
I. Ahmad, M. Basheri, M. J. Iqbal, and A. Rahim, “Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection,” IEEE Access, vol. 6, pp. 33789–33795, 2018.
A. Hasan, S. Moin, A. Karim, and S. Shamshirband, “Machine learning-based sentiment analysis for twitter accounts,” Math. Comput. Appl., vol. 23, no. 1, p. 11, 2018.
S. Rani, Ed., Sentiment Analysis on Twitter Data using Support Vector Machine, vol. 7, no. 3. 2016.
H. Parveen and S. Pandey, “Sentiment analysis on Twitter Data-set using Naive Bayes algorithm,” in 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 2016.
V. Effendy, “Sentiment analysis on Twitter about the use of city public transportation using support vector machine method,” Int. J. Inf. Commun. Technol. (IJoICT), vol. 2, no. 1, p. 57, 2016.
U. Kumari, A. K. Sharma, and D. Soni, “Sentiment analysis of smart phone product review using SVM classification technique,” in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017.
P. H. Prastyo, A. S. Sumi, A. W. Dian, and A. E. Permanasari, “Tweets responding to the Indonesian government’s handling of COVID-19: Sentiment analysis using SVM with Normalized Poly Kernel,” J. inf. syst. eng. bus. intell., vol. 6, no. 2, p. 112, 2020.
N. O. F. Daeli and A. Adiwijaya, “Sentiment analysis on movie reviews using Information gain and K-nearest neighbor,” 2020.
S. Fransiska, Rianto, and A. I. Gufroni, Eds., Sentiment Analysis Provider by. U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method, vol. 7, no. 2. Scientific Journal of Informatis, 2020.
B. Pratama et al., “Sentiment analysis of the Indonesian police mobile brigade corps based on twitter posts using the SVM and NB methods,” J. Phys. Conf. Ser., vol. 1201, no. 1, p. 012038, 2019.
M. Guia, R. Silva, and J. Bernardino, “Comparison of naïve Bayes, support vector machine, decision trees and random forest on sentiment analysis,” in Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2019.
A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of sentiment reviews using n-gram machine learning approach,” Expert Syst. Appl., vol. 57, pp. 117–126, 2016.
V. Raunak, V. Gupta, and F. Metze, “Effective Dimensionality Reduction for Word Embeddings,” in Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), 2019.
K. D. Yonatha Wijaya and A. A. I. N. E. Karyawati, “The effects of different kernels in SVM sentiment analysis on mass social distancing,” JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), vol. 9, no. 2, p. 161, 2020.
R. Mukarramah, D. Atmajaya, and L. B. Ilmawan, “Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter,” Ilk. J. Ilm., vol. 13, no. 2, pp. 168–174, 2021.
P. H. Prastyo, I. Ardiyanto, and R. Hidayat, “Indonesian sentiment analysis: An experimental study of four kernel functions on SVM algorithm with TF-IDF,” in 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 2020.
H. Li, X. Li, D. Caragea, and C. Caragea, Eds., Comparison of Word Embeddings and Sentence Encodings as Generalized Representations for Crisis Tweet Classification Tasks. Proceedings of ISCRAM Asia Pacific, 2018.
Downloads
Published
Versions
- 2024-05-21 (3)
- 2024-05-08 (2)
- 2023-03-16 (1)
Issue
Section
License
Authors who publish with this journal agree to the following terms:
(1) Copyright of the published articles will be transferred to the journal as the publisher of the manuscripts. Therefore, the author confirms that the copyright has been managed by the publisher.
(2) Publisher of TELKATTKA is Openlibrary Telkom University
(3) The copyright follows Creative Commons Attribution–ShareAlike License (CC BY SA): This license allows to Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material, for any purpose, even commercially.
