Aspect Based Sentiment Analysis with FastText Feature Expansion and Support Vector Machine Method on Twitter
Social media such as Twitter has now become very close to society. Twitter users can express current issues, their opinions, product reviews, and many other things both positive and negative. Twitter is also used by companies to monitor the assessment of their products among the public as insight that will be used to evaluate what aspects of their products need to be further developed. Twitter with its limitation of only allowing users to post a maximum tweet of 280 characters will make a lot of abbreviated and difficult to understand words used, so it will allow vocabulary mismatch problems to occur. Therefore, in this paper, research conducted on aspect-based sentiment analysis of Telkomsel’s products from the aspects of signal and service by applying feature expansion using Fasttext word embedding to overcome vocabulary mismatch problem and classification with the Support Vector Machine (SVM) method. Sampling technique with Synthetic Minority Oversampling Technique (SMOTE) used to overcome data imbalance. The experimental results show that feature expansion can increase the performance of model. The final results obtained F1-Score value of the model for the signal aspect increased by 27.91% with F1-Score 95.93%, and for the service aspect increased by 42.36% with F1-Score 94.53%.
J. E. Sembodo, E. B. Setiawan, and Z. K. Abdurahman Baizal, “The improvement of Indonesian news curator classification in Twitter,” 2017 5th Int. Conf. Inf. Commun. Technol. ICoIC7 2017, vol. 0, no. c, 2017, doi: 10.1109/ICoICT.2017.8074658.
E. B. Setiawan, D. H. Widyantoro, and K. Surendro, “Feature Expansion using Word Embedding for Tweet Topic Classification,” 2016 10th Int. Conf. Telecommun. Syst. Serv. Appl., no. 2011, 2016, doi: 10.1109/TSSA.2016.7871085.
S. Thavareesan and S. Mahesan, “Sentiment Analysis in Tamil Texts: A Study on Machine Learning Techniques and Feature Representation,” 2019 IEEE 14th Int. Conf. Ind. Inf. Syst. Eng. Innov. Ind. 4.0, ICIIS 2019 - Proc., pp. 320–325, 2019, doi: 10.1109/ICIIS47346.2019.9063341.
Samsir, Kusmanto, Abdul Hakim Dalimunthe, Rahmad Aditiya, and Ronal Watrianthos, “Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 1, pp. 1–6, Jun. 2022
P. Mehta and S. Pandya, “A review on sentiment analysis methodologies, practices and applications,” Int. J. Sci. Technol. Res., vol. 9, no. 2, pp. 601–609, 2020.
R. Hajrizi and K. P. Nuçi, “Aspect-Based Sentiment Analysis in Education Domain,” no. October, 2020, [Online]. Available: http://arxiv.org/abs/2010.01429.
H. S. Batubara, Ambiyar, Syahril, Fadhilah, and R. Watrianthos, “Sentiment Analysis of Face-To-Face Learning Based on Social Media,” Jurnal Pendidikan Teknologi Kejuruan, vol. 4, no. 3, pp. 102–106, 2021
N. U. Pannala, C. P. Nawarathna, J. T. K. Jayakody, L. Rupasinghe, and K. Krishnadeva, “Supervised learning based approach to aspect based sentiment analysis,” Proc. - 2016 16th IEEE Int. Conf. Comput. Inf. Technol. CIT 2016, 2016 6th Int. Symp. Cloud Serv. Comput. IEEE SC2 2016 2016 Int. Symp. Secur. Priv. Soc. Netwo, pp. 662–666, 2017, doi: 10.1109/CIT.2016.107.
Irbah Salsabila and Yuliant Sibaroni, “Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 3, pp. 520–526, 2021, doi: 10.29207/resti.v5i3.3078.
R. Watrianthos, M. Giatman, W. Simatupang, R. Syafriyeti, and N. K. Daulay, “Analisis Sentimen Pembelajaran Campuran Pada Twitter Data Menggunakan Algoritma Naïve Bayes,” Analisis Sentimen Pembelajaran Campuran Pada Twitter Data Menggunakan Algoritma Naïve Bayes, vol. 6, no. 1, pp. 166–170, 2022, doi: http://dx.doi.org/10.30865/mib.v6i1.3383.
J. W. Iskandar and Y. Nataliani, “Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1120–1126, 2021, doi: 10.29207/resti.v5i6.3588.
I. Kaibi, E. H. Nfaoui, and H. Satori, “A comparative evaluation of word embeddings techniques for twitter sentiment analysis,” 2019 Int. Conf. Wirel. Technol. Embed. Intell. Syst. WITS 2019, pp. 1–4, 2019, doi: 10.1109/WITS.2019.8723864.
Samsir, Ambiyar, U. Verawardina, F. Edi, and R. Watrianthos, “Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19,” JURNAL MEDIA INFORMATIKA BUDIDARMAJURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 10, pp. 174–179, 2021, doi: 10.30865/mib.v4i4.2293.
B. Gupta, M. Negi, K. Vishwakarma, G. Rawat, and P. Badhani, “Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python,” Int. J. Comput. Appl., vol. 165, no. 9, 2017, doi: 10.5120/ijca2017914022.
Alvi Rahmy Royyan and Erwin Budi Setiawan, “Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in Twitter,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, pp. 78–84, 2022, doi: 10.29207/resti.v6i1.3525.
N. A. N and E. B. Setiawan, “Implementation Word2Vec for Feature Expansion in Twitter Sentiment Analysis,” no. 10, pp. 837–842, 2021.
N. Nedjah, I. Santos, and L. de Macedo Mourelle, “Sentiment analysis using convolutional neural network via word embeddings,” Evol. Intell., pp. 2–6, 2019, doi: 10.1007/s12065-019-00227-4.
A. Nurdin, B. Anggo Seno Aji, A. Bustamin, and Z. Abidin, “Perbandingan Kinerja Word Embedding Word2Vec, Glove, Dan Fasttext Pada Klasifikasi Teks,” J. Tekno Kompak, vol. 14, no. 2, p. 74, 2020, doi: 10.33365/jtk.v14i2.732.
S. K. Lidya, O. S. Sitompul, and S. Efendi, “Sentiment Analysis Pada Teks Bahasa Indonesia Menggunakan Support Vector Machine ( Svm ),” Semin. Nas. Teknol. dan Komun. 2015, vol. 2015, no. Sentika, pp. 1–8, 2015, doi: 10.1016/j.eswa.2013.08.047.
Oryza Habibie Rahman, Gunawan Abdillah, and Agus Komarudin, “Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 17–23, 2021, doi: 10.29207/resti.v5i1.2700.
G. D. Salsabila and E. B. Setiawan, “Semantic Approach for Big Five Personality Prediction on Twitter,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 680–687, 2021, doi: 10.29207/resti.v5i4.3197.
A. R. D. Pratiwi and E. B. Setiawan, “Implementation of Rumor Detection on Twitter Using the SVM Classification Method,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 5, pp. 782–789, 2021, doi: 10.29207/resti.v4i5.2031.
M. Ibrahim, M. Torki, and N. El-Makky, “Imbalanced Toxic Comments Classification Using Data Augmentation and Deep Learning,” Proc. - 17th IEEE Int. Conf. Mach. Learn. Appl. ICMLA 2018, pp. 875–878, 2019, doi: 10.1109/ICMLA.2018.00141.
Copyright (c) 2022 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;