Aspect Category Classification with Machine Learning Approach using Indonesian Language Dataset
Abstract
Customer reviews are opinions on the quality of goods or services that consumers perceive. Customer reviews contain useful information for both consumers and providers of goods or services. The availability of a large number of customer reviews on the websiterequires a framework for extracting sentiment automatically. A customer review often contains many aspects, so the Aspect Based Sentiment Analysis (ABSA) should be used to determine the polarity of each aspect. One of the important tasks in ABSA is Aspect Category Detection. The application of Machine Learning Methods for Aspect Category Detection has been mostly done in the English language domain, but in the Indonesian language domain,there are still a few. This study compares the performance of three machine learning algorithms, namely Naïve Bayes (NB), Support Vector Machine (SVM),and Random Forest (RF),on Indonesian language customer reviews using Term Frequency-Inverse Document Frequency (TF-IDF) as term weighting. The results showthat RFperformsthe best,compared to NB and SVM,in three different domains, namely restaurants, hotels,and e-commerce,with the f1-scoresfor each domainare84.3%, 85.7%, and 89.3%.
References
D.F. Nasiri dan I. Budi, “Aspect Category Detection on Indonesian E-commerce Mobile Application Review,” Int. Conf. Data Softw. Eng., 2019, hal. 1–6.
B. Liu, Sentiment Analysis and Opinion Mining, Chicago, USA: Morgan and Claypool Publisher, 2012.
T. Alvarez-López, J. Juncal-Martínez, M. Fernández-Gavilanes, E. Costa-Montenegro, dan F.J. González-Castã, “SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis,” Proc. 10th Int. Work. Semant. Eval., 2016, hal. 306–311.
M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos, dan S. Manandhar, “SemEval-2014 Task 4: Aspect Based Sentiment Analysis,” Proc. 8th Int. Work. Semant. Eval., 2015, hal. 27–35.
H. Papageorgiou, I. Androutsopoulos, D. Galanis, M. Pontiki, dan S. Manandhar, “SemEval-2015 Task 12: Aspect Based Sentiment Analysis,” Proc. 9th Int. Work. Semant. Eval., 2015, hal. 486–495.
M. Pontiki, D. Galanis, H. Papageorgiou, dkk., “SemEval-2016 Task 5: Aspect Based Sentiment Analysis,” Proc. 10th Int. Work. Semant. Eval., 2016, hal. 19–30.
S. Movahedi, E. Ghadery, H. Faili, dan A. Shakery, “Aspect Category Detection via Topic-Attention Network,” arXiv Prepr. arXiv1901.01183, hal. 1–9, 2019.
M. Afzaal, M. Usman, dan A. Fong, “Tourism Mobile App with Aspect-Based Sentiment Classification Framework for Tourist Reviews,” IEEE Trans. Consum. Electron., Vol. 65, No. 2, hal. 233–242, 2019.
M.S. Mubarok, A. Adiwijaya, dan M.D. Aldhi, “Aspect-based Sentiment Analysis to Review Products Using Naïve Bayes,” AIP Conf. Proc., Vol. 1867, No. 1, hal. 1–8, 2017.
A. Bhoi dan S. Joshi, “Various Approaches to Aspect-based Sentiment Analysis,” arXiv:1805.01984, hal. 1-3, 2018.
S. Gojali dan M. L. Khodra, “Aspect Based Sentiment Analysis for Review Rating Prediction,” Int. Conf. Adv. Informatics Concepts, Theory Appl., 2016, hal. 1–6.
D. Ekawati dan M.L. Khodra, “Aspect-based Sentiment Analysis for Indonesian Restaurant Reviews,” Int. Conf. Adv. Informatics Concepts, Theory Appl., 2017, hal. 1–6.
A. Cahyadi dan M.L. Khodra, “Aspect-Based Sentiment Analysis Using Convolutional Neural Network and Bidirectional Long Short-Term Memory,” Int. Conf. Adv. Informatics Concept Theory Appl., 2018, hal. 124–129.
Z. Fachrina dan D.H. Widyantoro, “Aspect-Sentiment Classification in Opinion Mining using the Combination of Rule-Based and Machine Learning,” Int. Conf. Data Softw. Eng., 2017, hal. 1–6.
A. Ilmania, S. Cahyawijaya, Abdurrahman, dan A. Purwarianti, “Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-Based Sentiment Analysis,” Int. Conf. Asian Lang. Process., 2018, hal. 62–67.
Maulana, “Ringkasan Jumlah Aspek Ulasan Hotel untuk Pembentukan Dataset Sentimen Analisis Berbasis Aspek,” J. Linguist. Komputasional, Vol. 3, No. 2, hal. 62–66, 2020.
M.A. Fauzi, “Random Forest Approach for Sentiment Analysis in Indonesian Language,” Indones. J. Electr. Eng. Comput. Sci., Vol. 12, No. 1, hal. 46–50, 2019.
(2018) GITHUB website. [Online], https://github.com/PratamaAgung/AspectBasedSentimentAnalysis, tanggal akses: 22-Jan-2020.
(2019) GITHUB website. [Online], https://github.com/annisanurulazhar/absa-playground, tanggal akses: 11-Feb-2021.
M.S. Akhtar, D. Gupta, A. Ekbal, dan P. Bhattacharyya, “Feature Selection and Ensemble Construction: A Two-step Method for Aspect Based Sentiment Analysis,” Knowledge-Based Syst., Vol. 125, hal. 116–135, 2017.
D. Tang, B. Qin, dan T. Liu, “Aspect Level Sentiment Classification with Deep Memory Network,” Conf. Empir. Methods Nat. Lang. Process., 2016, hal. 214–224.
A. Mridula dan C.R. Kavitha, “Opinion Mining and Sentiment Study of Tweets Polarity Using Machine Learning,” 2018 2nd Int. Conf. Inven. Commun. Comput. Technol., 2018, hal. 621–626.
N.U. Pannala, C.P. Nawarathna, J.T.K. Jayakody, L. Rupasinghe, dan K. Krishnadeva, “Supervised Learning Based Approach to Aspect Based Sentiment Analysis,” Int. Conf. Comput. Inf. Technol., 2016, hal. 662–666.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
1.png)
