SAER : Comparison of Rule Prediction Algorithms on Constructing a Corpus for Taxation Related Tweet Aspect-Based Sentiment Analysis

Authors

  • Annisa Mufidah Sopian 3Department of Informatics, Faculty of Science and Informatics, Universitas Jenderal Achmad Yani, Indonesia
  • Ridwan Ilyas 3Department of Informatics, Faculty of Science and Informatics, Universitas Jenderal Achmad Yani, Indonesia
  • Fatan Kasyidi 3Department of Informatics, Faculty of Science and Informatics, Universitas Jenderal Achmad Yani, Indonesia
  • Asep Id Hadiana Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

DOI:

https://doi.org/10.15575/join.v9i1.1275

Keywords:

Taxation, Aspect-Opinion Extraction, Rule Prediction , SAER, Syntactic feature

Abstract

Twitter is a popular social media in Indonesia, and sentiment analysis on Twitter has an important role in measuring public trust, especially in taxation issues. Aspect extraction is an important task in sentiment analysis. In this research, we propose SAER, a Syntactic Aspect-opinion Extraction and Rule prediction, that used language rule-based approach using syntactic features for aspect and opinion extraction, and we compare several algorithm for rule prediction such as Random Forest Regression, Decision Tree Regression, K-Nearest Neighbor Regression (KNN), Linear Regression, Support Vector Regression (SVR), and Extreme Gradient Boosting Regression (XGBoost) that can generate rules with a tree-based approach. By employing syntactic features and rule prediction, it has been able to explore important features in a sentence. In rule prediction, comparison results show that Support Vector Regression (SVR) was identified as the most effective model for aspects rule prediction, providing the best results with a Mean Squared Error (MSE) of 0.022, Root Mean Squared Error (RMSE) of 0.150, and Mean Absolute Error (MAE) of 0.123. While XGBoost was identified as the most effective model for opinions rule prediction, with MSE of 0.013, RMSE of 0.117, and MAE of 0.075. Since we used syntactic feature-based approaches and rule prediction in this work, it is expected to be implemented for other cases, with other domain datasets.

References

N. Sholihah, F. F. Abdulloh, M. Rahardi, A. Aminuddin, B. P. Asaddulloh, and A. Y. A. Nugraha, “Feature Selection Optimization for Sentiment Analysis of Tax Policy Using SMOTE and PSO,” in 2023 3rd International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS), IEEE, Dec. 2023, pp. 44–48. doi: 10.1109/ICON-SONICS59898.2023.10435269.

S. A. Mirlohi Falavarjani, J. Jovanovic, H. Fani, A. A. Ghorbani, Z. Noorian, and E. Bagheri, “On the causal relation between real world activities and emotional expressions of social media users,” J. Assoc. Inf. Sci. Technol., vol. 72, no. 6, pp. 723–743, Jun. 2021, doi: 10.1002/asi.24440.

M. A. Saputra and E. B. Setiawan, “Aspect Based Sentiment Analysis Using Recurrent Neural Networks (RNN) on Social Media Twitter,” in 2023 International Conference on Data Science and Its Applications (ICoDSA), IEEE, Aug. 2023, pp. 265–270. doi: 10.1109/ICoDSA58501.2023.10276768.

K. T. Shandana, A. Aminuddin, E. H. Saputra, F. F. Abdulloh, M. Rahardi, and B. P. Asaddulloh, “Sentiment Analysis of Google Classroom Application Using Machine Learning Techniques,” in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), IEEE, Nov. 2023, pp. 954–959. doi: 10.1109/ICAMIMIA60881.2023.10427706.

F. Z. Ruskanda, D. H. Widyantoro, and A. Purwarianti, “Sequential Covering Rule Learning for Language Rule-based Aspect Extraction,” in 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS), IEEE, Oct. 2019, pp. 229–234. doi: 10.1109/ICACSIS47736.2019.8979743.

F. Z. Ruskanda, D. H. Widyantoro, and A. Purwarianti, “Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning,” J. ICT Res. Appl., vol. 14, no. 1, p. 51, Jul. 2020, doi: 10.5614/itbj.ict.res.appl.2020.14.1.4.

P. Ray and A. Chakrabarti, “A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis,” Appl. Comput. Informatics, vol. 18, no. 1/2, pp. 163–178, Mar. 2022, doi: 10.1016/j.aci.2019.02.002.

M. K. Dadap et al., “Aspect-Based Sentiment Analysis Applied in the News Domain Using Rule-Based Aspect Extraction and BiLSTM,” in 2023 IEEE 6th International Conference on Computer and Communication Engineering Technology (CCET), IEEE, Aug. 2023, pp. 22–26. doi: 10.1109/CCET59170.2023.10335116.

D. Kun Indarta and A. Romadhony, “Aspect and Opinion Extraction of Indonesian Lipsticks Product Reviews using Conditional Random Field (CRF),” in 2021 13th International Conference on Knowledge and Smart Technology (KST), IEEE, Jan. 2021, pp. 113–117. doi: 10.1109/KST51265.2021.9415829.

T. U. Tran, H. T. Thi Hoang, and H. X. Huynh, “Aspect Extraction with Bidirectional GRU and CRF,” in 2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF), IEEE, Mar. 2019, pp. 1–5. doi: 10.1109/RIVF.2019.8713663.

A. F. Pathan and C. Prakash, “Unsupervised Aspect Extraction Algorithm for opinion mining using topic modeling,” Glob. Transitions Proc., vol. 2, no. 2, pp. 492–499, Nov. 2021, doi: 10.1016/j.gltp.2021.08.005.

S. J. Das and B. Chakraborty, “An Approach for Automatic Aspect Extraction by Latent Dirichlet Allocation,” in 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), IEEE, Oct. 2019, pp. 1–6. doi: 10.1109/ICAwST.2019.8923417.

S. Li, R. Wang, and G. Zhou, “Opinion Target Extraction Using a Shallow Semantic Parsing Framework,” Proc. AAAI Conf. Artif. Intell., vol. 26, no. 1, pp. 1671–1677, Sep. 2021, doi: 10.1609/aaai.v26i1.8346.

D. E. Cahyani, F. F. Setyawan, A. D. Hariadi, L. Gumilar, and A. K. Junoh, “Comparison of Regression Methods for Estimation of State-of-Health in Lithium-Ion Batteries,” in 2023 International Conference on Electrical and Information Technology (IEIT), IEEE, Sep. 2023, pp. 202–206. doi: 10.1109/IEIT59852.2023.10335524.

E. Brilliandy, H. Lucky, A. Hartanto, D. Suhartono, and M. Nurzaki, “Using Regression to Predict Number of Tourism in Indonesia based of Global COVID-19 Cases,” in 2022 3rd International Conference on Artificial Intelligence and Data Sciences (AiDAS), IEEE, Sep. 2022, pp. 310–315. doi: 10.1109/AiDAS56890.2022.9918731.

I. Sabilirrasyad, Z. Hasan, and M. Hermansyah, “Sentiment Analysis of Twitter Discussions on Rafael Alun: Multinomial Naïve Bayes and Decision Tree Approach,” PROCEEDING Int. Conf. Econ. Bus. Inf. Technol., vol. 4, pp. 803–809, Jul. 2023, doi: 10.31967/prmandala.v4i0.827.

R. Gusdiana, I. Alfian, and C. Juliane, “IMPLEMENTATION OF TEXT PROCESSING FOR SENTIMENT ANALYSIS OF TAX PAYMENT INTEREST AFTER THE ‘RUBICON’ PHENOMENON,” J. Tek. Inform., vol. 4, no. 5, pp. 1157–1164, Oct. 2023, doi: 10.52436/1.jutif.2023.4.5.1014.

D. Gunawan, H. P. Siregar, and O. Salim Sitompul, “Identifying Sentence Structure in Bahasa Indonesia by Using POS Tag and LALR Parser,” in 2019 5th International Conference on Computing Engineering and Design (ICCED), IEEE, Apr. 2019, pp. 1–5. doi: 10.1109/ICCED46541.2019.9161125.

F. Z. Ruskanda, A. Purwarianti, and D. H. Widyantoro, “Dependency-Based Feature and Pairwise Classifier for Cross-Domain Aspect Term Extraction,” SSRN Electron. J., 2022, doi: 10.2139/ssrn.4156881.

H. Yufeng and Z. Fenglu, “A Rich-label Constituency Tree for Constituency Parsing,” in 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), IEEE, May 2019, pp. 186–190. doi: 10.1109/ITAIC.2019.8785583.

A. S. Shafie, N. M. Sharef, M. A. Azmi Murad, and A. Azman, “Aspect Extraction Performance with POS Tag Pattern of Dependency Relation in Aspect-based Sentiment Analysis,” in 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), IEEE, Mar. 2018, pp. 1–6. doi: 10.1109/INFRKM.2018.8464692.

A. Khalid, A. Sundararajan, and A. Sarwat, “A Statistical out-of-Sample Forecast to Estimate Lithium-Ion Parameters That Determine State of Charge,” ECS Meet. Abstr., vol. MA2019-04, no. 4, pp. 208–208, Jun. 2019, doi: 10.1149/MA2019-04/4/208.

“KBBI VI Daring.” Accessed: Mar. 24, 2024. [Online]. Available: https://kbbi.kemdikbud.go.id/entri/aspek

“KBBI VI Daring.” Accessed: Mar. 24, 2024. [Online]. Available: https://kbbi.kemdikbud.go.id/entri/opini

Downloads

Published

2024-05-08 — Updated on 2024-05-08

Versions

Issue

Section

Article

Citation Check