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Sentiment Analysis of Customer Reviews Using Support Vector Machine and Smote-Tomek Links For Identify Customer Satisfaction

*Dody Indra Sumantiawan  -  Magister Sistem Informasi, Sekolah Pascasarjana Universitas Diponegoro, Jl. Imam Bardjo SH No.5, Pleburan, Kec. Semarang Sel., Kota Semarang, Jawa Tengah 50241, Indonesia
Jatmiko Endro Suseno  -  Physics Department, Faculty of Science and Mathematics Diponegoro University, Indonesia
Wahyul Amien Syafei  -  Department of Electrical Engineering, Faculty of Engineering Diponegoro University, Indonesia
Open Access Copyright (c) 2023 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract

Shopping activities in the online market, especially fashion trends, continue to increase with all the promo efforts offered. One of the considerations for buying products on the online market is to read reviews. Each consumer review shows the level of interest in the product. The number of negative reviews and the emergence of many varied reviews pose a problem in categorizing reviews. Sentiment analysis is a way of looking at the polarity of reviews to classify positive and negative reviews. The Support Vector Machine method and the combination of the Synthetic Minority Oversampling Technique (SMOTE) with Tomek Links are applied in this study. Classification using the Support Vector Machine method and the combination of the Synthetic Minority Oversampling Technique (SMOTE) with Tomek Links showed better results with an Accuracy of 0.92, Precision of 0.89, Recall of 0.89, and F1-score of 0.89 than without the combination of the Synthetic Minority oversampling Technique (SMOTE) with Tomek Links with an Accuracy of 0.68, Precision of 0.55, Recall of 0.99, and an F1-score of 0.71.

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Keywords: Sentiment Analysis, Classification, Support Vector Machine, SMOTE, Tomek Links

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  1. Arivoli, Sonali., 2021. Sentiment Analysis Using Support Vector Machine Based On Feature Selection and Semantic Analysis, International Research Journal of Computer Science 7 (8), 209-214
  2. Batista, G. E., Bazzan, A. L., Monard, M. C., 2003. Balancing training data for automated annotation of keywords: a case study. In WOB 10-18
  3. Bin Alias, M.S., Ibrahim, N.B., Zin, Z.B., 2021. Improved sampling data workflow using smtmk to increase the classification accuracy of imbalanced dataset. European Journal of Molecular and Clinical Medicine 8 (2), 91–99
  4. Baek, Y., Yun, U., Kim, H., Nam, H., Lee, G., Yoon, E., Vo, B., Lin, J.C.W., 2020. Erasable pattern mining based on tree structures with damped window over data streams. Engineering Applications of Artificial Intelligence 94, 103735
  5. Borg, A., Boldt, M., 2020. Using VADER sentiment and SVM for predicting customer response sentiment. Expert Systems with Applications 162113746
  6. Chapelle, G., Eymeoud, J.B., 2022. Can big data increase our knowledge of local rental markets? A dataset on the rental sector in France. PLoS ONE 17, 1–21
  7. Cortes C., Vapnik V., 1995, Support-vector networks. Machine Learn 20 (3), 273–297
  8. Elhassan AT., Aljourf M., Al-Mohanna F., Shoukri M., 2016. Classification of imbalance data using tomek link (t-link) combined with random under-sampling (RUS) as a data reduction method, Global Journal of Technology & Optimization, 1-11
  9. Gorunescu, F., 2011. Data Mining Concepts, Models and Techniques. Springer, Berlin
  10. Hadwan, M., Al-Sarem, M., Saeed, F., Al-Hagery, M.A., 2022. An improved sentiment classification approach for measuring user satisfaction toward governmental services’ mobile apps using machine learning methods with feature engineering and SMOTE technique. Applied Sciences 12 (11), 5547
  11. Hunt, I., 2021. In-sample tests of predictability are superior to pseudo-out-of-sample tests, even when data mining. International Journal of Forecasting 150156–154
  12. Lee, L.-H., Chen, C.-H., Chang, W.-C., Lee, P.-L., Shyu, K.-K., Chen, M.-H., Hsu, J.-W., Bai, Y.-M., Su, T.-P., Tu, P.-C., 2022. Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants. European Psychiatry 65 (1)
  13. Li, X., Wu, C., Mai, F., 2019. The effect of online reviews on product sales: A joint sentiment-topic analysis. Information and Management 56 (2), 172–184
  14. Mustopa, A., Hermanto, Anna, Pratama, E.B., Hendini, A., Risdiansyah, D., 2020. Analysis of user reviews for the pedulilindungi application on google play using the support vector machine and naive bayes algorithm based on particle swarm optimization. 2020 5th International Conference on Informatics and Computing, ICIC 2
  15. Obiedat, R., Qaddoura, R., Al-Zoubi, A.M., Al-Qaisi, L., Harfoushi, O., Alrefai, M., Faris, H., 2022. Sentiment analysis of customers’ reviews using a hybrid evolutionary SVM-based approach in an imbalanced data distribution. IEEE Access 1022260–22273
  16. Sain, H., Purnami, S.W., 2015. Combine sampling support vector machine for imbalanced data classification. Procedia Computer Science 7259–66
  17. Sasada, T., Liu, Z., Baba, T., Hatano, K., Kimura, Y., 2020. A resampling method for imbalanced datasets considering noise and overlap. Procedia Computer Science 176420–429
  18. Singla, Z., Randhawa, S., Jain, S., 2017. Sentiment analysis on product reviews using machine learning techniques. 2017 International Conference on Intelligent Computing and Control (I2C2)
  19. Swana, E.F., Doorsamy, W., Bokoro, P., 2022. Tomek link and SMOTE approaches for machine fault classification with an imbalanced dataset. Sensors 22 (9)
  20. Wang, C., Chen, J., Chen, X., 2017. Pricing and order decisions with option con- tracts in the presence of customer returns. International Journal of Production Economics 193(1), 422-436
  21. Wang, Q., Zhang, W., Li, J., Mai, F., Ma, Z., 2022. Effect of online review sentiment on product sales: the moderating role of review credibility perception. Computers in Human Behavior 133, 107272

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