Skip to main navigation menu Skip to main content Skip to site footer
International Journal of Robotics and Automation Technology

Articles

Vol. 11 (2024)

Consumer Feedback Sentiment Classification Improved Via Modified Metaheuristic Optimization Natural Language Processing

DOI
https://doi.org/10.31875/2409-9694.2024.11.07
Submitted
November 9, 2024
Published
09.11.2024

Abstract

This study investigates the synergy between the virtual and real-world economies through e-commerce, where seller reputation is critical in guiding consumer decisions. As traditional businesses shift towards online retail, user reviews become essential, offering feedback to both sellers and potential buyers. Sentiment analysis through machine learning (ML) techniques presents significant advantages for consumers and retailers alike. This research proposes a novel approach combining bidirectional encoder representations from transformers (BERT) embeddings with an optimized XGBoost classification model to enhance sentiment analysis performance. A modified metaheuristic algorithm, derived from the firefly algorithm (FA), is introduced to optimize the model. Testing on publicly available datasets demonstrates that models optimized by this algorithm achieved a peak accuracy of .881336. Further statistical analyses substantiate these improvements, and SHAP interpretation on the best-performing model identifies key features impacting model predictions, shedding light on factors driving customer sentiment insights.

References

  1. Bacanin, N., Zivkovic, M., Hajdarevic, Z., Janicijevic, S., Dasho, A., Marjanovic, M., Jovanovic, L.: Performance of sine cosine algorithm for ann tuning and training for iot security. In: International Conference on Hybrid Intelligent Systems. pp. 302-312. Springer (2022). https://doi.org/10.1007/978-3-031-27409-1_27
  2. Bozovic, A., Jovanovic, L., Desnica, E., Radovanovic, L., Zivkovic, M., Bacanin, N.: Direct current motor malfunction detection through metastatic optimized audio analysis and classification. In: 2024 International Conference on Inventive Computation Technologies (ICICT). pp. 1471-1478. IEEE (2024). https://doi.org/10.1109/ICICT60155.2024.10544852
  3. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: North American Chapter of the Association for Computational Linguistics (2019). https://api.semanticscholar.org/CorpusID:52967399
  4. Djuric, M., Jovanovic, L., Zivkovic, M., Bacanin, N., Antonijevic, M., Sarac, M.: The adaboost approach tuned by sns metaheuristics for fraud detection. In: Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences: PCCDS 2022. pp. 115-128. Springer (2023). https://doi.org/10.1007/978-981-19-8742-7_10
  5. Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process 5(2), 1 (2015). https://doi.org/10.5121/ijdkp.2015.5201
  6. Hubalovska, M., Hubalovsky, S., Trojovsky, P.: Botox optimization algorithm: A new human-based metaheuristic algorithm for solving optimization problems. Biomimetics 9(3) (2024)., https://www.mdpi.com/2313-7673/9/3/137. https://doi.org/10.3390/biomimetics9030137
  7. Jia, H., Rao, H., Wen, C., Mirjalili, S.: Crayfish optimization algorithm. Artificial Intelligence Review 56(2), 1919–1979 (Nov 2023). https://doi.org/10.1007/s10462-023-10567-4
  8. Jovanovic, A., Dogandzic, T., Jovanovic, L., Kumpf, K., Zivkovic, M., Bacanin, N.: Metaheuristic optimized bilstm univariate time series forecasting of gold prices. In: International Conference on Data Science and Applications. pp. 221–235. Springer (2023). https://doi.org/10.1007/978-981-99-7862-5_17
  9. Jovanovic, L., Antonijevic, M., Zivkovic, M., Dobrojevic, M., Salb, M., Strumberger, I., Bacanin, N.: Long short-term memory tuning by enhanced harris hawks optimization algorithm for crude oil price forecasting (2024). https://doi.org/10.1016/bs.adcom.2024.01.002
  10. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks. vol. 4, pp. 1942–1948 vol.4 (1995). https://doi.org/10.1109/ICNN.1995.488968
  11. Kozakijevic, S., Salb, M., Elsadai, A., Mani, J., Devi, K., Sharko, A.D., Muthusamy, S.: Seizure detection via time series classification using modified metaheuristic optimized recurrent networks. Theoretical and Applied Computational Intelligence 1(1), 82-94 (2023). https://doi.org/10.31181/taci1120238
  12. Kumpf, K., Kozakijevic, S., Jovanovic, L., Cajic, M., Zivkovic, M., Bacanin, N.: A two layer hybrid approach for parkinson’s disease detection optimized via modified metaheuristic algorithm. In: International Conference on Innovations and Advances in Cognitive Systems. pp. 205-219. Springer (2024). https://doi.org/10.1007/978-3-031-69197-3_16
  13. LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., Herrera, F.: A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation 67, 100973 (2021)., https://www.sciencedirect.com/science/article/pii/S2210650221001358. https://doi.org/10.1016/j.swevo.2021.100973
  14. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. p. 4768–4777. NIPS’17, Curran Associates Inc., Red Hook, NY, USA (2017)
  15. Markovic, I., Krzanovic, J., Jovanovic, L., Toskovic, A., Bacanin, N., Petrovic, A., Zivkovic, M.: Flood prediction based on recurrent neural network time series classification boosted by modified metaheuristic optimization. In: International Conference on Advances in Data-driven Computing and Intelligent Systems. pp. 289-303. Springer (2023). https://doi.org/10.1007/978-981-99-9518-9_21
  16. Medsker, L., Jain, L.C.: Recurrent neural networks: design and applications. CRC press (1999). https://doi.org/10.1201/9781420049176
  17. Milicevic, M., Jovanovic, L., Bacanin, N., Zivkovic, M., Jovanovic, D., Antonijevic, M., Savanovic, N., Strumberger, I.: Optimizing long short-term memory by improved teacher learning-based optimization for ethereum price forecasting. In: Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2023, pp. 125-139. Springer (2023). https://doi.org/10.1007/978-981-99-0835-6_9
  18. Mirjalili, S.: Genetic Algorithm, pp. 43-55. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-319-93025-1_4
  19. Mladenović, N., Hansen, P.: Variable neighborhood search. Computers & Operations Research 24(11), 1097-1100 (1997)., https://www.sciencedirect.com/science/article/pii/S0305054897000312. https://doi.org/10.1016/S0305-0548(97)00031-2
  20. Petrovic, A., Antonijevic, M., Strumberger, I., Jovanovic, L., Savanovic, N., Janicijevic, S.: The xgboost approach tuned by tlb metaheuristics for fraud detection. In: Proceedings of the 1st international conference on innovation in information technology and business (ICIITB 2022). vol. 104, p. 219. Springer Nature (2023). https://doi.org/10.2991/978-94-6463-110-4_16
  21. Petrovic, A., Jovanovic, L., Bacanin, N., Antonijevic, M., Savanovic, N., Zivkovic, M., Milovanovic, M., Gajic, V.: Exploring metaheuristic optimized machine learning for software defect detection on natural language and classical datasets. Mathematics 12(18), 2918 (2024). https://doi.org/10.3390/math12182918
  22. Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Quasi-oppositional differential evolution. In: 2007 IEEE congress on evolutionary computation. pp. 2229-2236. IEEE (2007). https://doi.org/10.1109/CEC.2007.4424748
  23. Schultz, B.B.: Levene’s Test for Relative Variation. Systematic Biology 34(4), 449-456 (12 1985). https://doi.org/10.1093/sysbio/34.4.449
  24. Shapiro, S.S., Francia, R.S.: An approximate analysis of variance test for normality. Journal of the American Statistical Association 67(337), 215-216 (1972)., https://www.tandfonline.com. https://doi.org/10.1080/01621459.1972.10481232
  25. Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020). https://doi.org/10.1016/j.physd.2019.132306
  26. Stankovic, M., Jovanovic, L., Antonijevic, M., Bozovic, A., Bacanin, N., Zivkovic, M.: Univariate individual household energy forecasting by tuned long short-term memory network. In: Inventive Systems and Control: Proceedings of ICISC 2023, pp. 403-417. Springer (2023). https://doi.org/10.1007/978-981-99-1624-5_30
  27. Topal, M.O., Bas, A., van Heerden, I.: Exploring transformers in natural language generation: Gpt, bert, and xlnet. arXiv preprint arXiv:2102.08036 (2021).
  28. Toskovic, A., Kozakijevic, S., Jovanovic, L., Zivkovic, M., Bacanin, N., Antonijevic, M.: Anomaly detection in electroencephalography readings using long short-term memory tuned by modified metaheuristic. In: International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering & Technology. pp. 133-148. Springer (2023). https://doi.org/10.1007/978-981-97-0327-2_10
  29. Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67-82 (1997). https://doi.org/10.1109/4235.585893
  30. Woolson, R.F.: Wilcoxon signed-rank test (2005)., https://onlinelibrary.wiley.com. https://doi.org/10.1002/0470011815.b2a15177
  31. Yang, X.S., He, X.: Bat algorithm: literature review and applications. International Journal of Bio-Inspired Computation 5(3), 141-149 (2013), https://www.inderscienceonline.com, pMID: 55093. https://doi.org/10.1504/IJBIC.2013.055093
  32. Yang, X.S., He, X.: Firefly algorithm: recent advances and applications. International journal of swarm intelligence 1(1), 36-50 (2013). https://doi.org/10.1504/IJSI.2013.055801
  33. Zivkovic, M., Jovanovic, L., Pavlov, M., Bacanin, N., Dobrojevic, M., Salb, M.: Optimized recurrent neural networks with attention for wind farm energy generation forecasting. In: 2023 16th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS). pp. 187–190. IEEE (2023). https://doi.org/10.1109/TELSIKS57806.2023.10316047

Most read articles by the same author(s)