Details

Title

Aspect-based sentiment classification model employing whale-optimized adaptive neural network

Journal title

Bulletin of the Polish Academy of Sciences: Technical Sciences

Yearbook

2021

Volume

69

Issue

3

Affiliation

Balaganesh, Nallathambi : Department of Computer Science & Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamilnadu, India ; Muneeswaran, K. : Department of Computer Science & Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamilnadu, India

Authors

Keywords

aspect-based sentiment analysis ; whale optimization algorithm ; artificial neural network ; opinion mining

Divisions of PAS

Nauki Techniczne

Coverage

e137271

Bibliography

  1.  L. Cui, S. Huang, F. Wei, C. Tan, C. Duan, and M. Zhou, “Superagent: A customer service chatbot for E-commerce websites,” in ACL 2017 – 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations, 2017, pp. 97–102, doi: 10.18653/v1/P17-4017.
  2.  M. Afzaal, M. Usman, and A. Fong, “Tourism mobile app with aspect-based sentiment classification framework for tourist reviews,” IEEE Trans. Consum. Electron. 65(2), 233–242, 2019, doi: 10.1109/TCE.2019.2908944.
  3.  M.S. Akhtar, T. Garg, and A. Ekbal, “Multi-task learning for aspect term extraction and aspect sentiment classification,” Neurocomputing 398, pp. 247–256, 2020, doi: 10.1016/j.neucom.2020.02.093.
  4.  M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos, and S. Manandhar, “SemEval-2014 Task 4: Aspect Based Sentiment Analysis,” in Proceedings ofthe 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, pp. 27–35, doi: 10.3115/v1/s14-2004.
  5.  M. Ghiassi, J. Skinner, and D. Zimbra, “Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network,” Expert Syst. Appl. 40(16), 6266–6282, 2013, doi: 10.1016/j.eswa.2013.05.057.
  6.  M. Mladenović, J. Mitrović, C. Krstev, and D. Vitas, “Hybrid sentiment analysis framework for a morphologically rich language,” J. Intell. Inf. Syst. 46(3), 599–620, 2016, doi: 10.1007/s10844-015-0372-5.
  7.  Y. Kai, Y. Cai, H. Dongping, J. Li, Z. Zhou, and X. Lei, “An effective hybrid model for opinion mining and sentiment analysis,” in IEEE International Conference on Big Data and Smart Computing, BigComp 2017, 2017, pp. 465–466, doi: 10.1109/BIGCOMP.2017.7881759.
  8.  F. Iqbal et al., “A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction,” IEEE Access 7, pp. 14637–14652, 2019, doi: 10.1109/ACCESS.2019.2892852.
  9.  J.R. Alharbi and W.S. Alhalabi, “Hybrid approach for sentiment analysis of twitter posts using a dictionary-based approach and fuzzy logic methods: Study case on cloud service providers,” Int. J. Semant. Web Inf. Syst. 16(1), 116–145, 2020, doi: 10.4018/IJSWIS.2020010106.
  10.  S.C. Cagan, M. Aci, B.B. Buldum, and C. Aci, “Artificial neural networks in mechanical surface enhancement technique for the prediction of surface roughness and microhardness of magnesium alloy,” Bull. Polish Acad. Sci. Tech. Sci. 67(4), 729–739, 2019, doi: 10.24425/ bpasts.2019.130182.
  11.  B. Paprocki, A. Pregowska, and J. Szczepanski, “Optimizing information processing in brain-inspired neural networks,” Bull. Polish Acad. Sci. Tech. Sci. 68(2), 225–233, 2020, doi: 10.24425/bpasts.2020.131844.
  12.  I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in ecodesign,” Bull. Polish Acad. Sci. Tech. Sci. 68(2), 199–206, 2020, doi: 10.24425/bpasts.2020.131848.
  13.  S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, “Optimization by simulated annealing,” Science 220(4598), 671–680, 1983, doi: 10.1126/ science.220.4598.671.
  14.  F.F. Moghaddam, R.F. Moghaddam, and M. Cheriet, “Curved Space Optimization: A Random Search based on General Relativity Theory,” pp. 1–16, 2012, [Online]. Available: http://arxiv.org/abs/1208.2214.
  15.  S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Adv. Eng. Softw. 95, pp. 51–67, 2016, doi: 10.1016/j.advengsoft.2016.01.008.
  16.  T. Brychcín, M. Konkol, and J. Steinberger, “UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis,” in Proc. 8th Int. Workshop Semantic Eval. (SemEval) (2014), 2015, no. SemEval, pp. 817–822, doi: 10.3115/v1/s14-2145.
  17.  J. Singh, G. Singh, and R. Singh, “Optimization of sentiment analysis using machine learning classifiers,” Human-centric Comput. Inf. Sci. 7(1), 2017, doi: 10.1186/s13673-017-0116-3.
  18.  M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, “Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews,” J. Comput. Sci. 27, pp. 386‒393, 2018, doi: 10.1016/j.jocs.2017.11.006.
  19.  P. Kalarani and S. Selva Brunda, “Sentiment analysis by POS and joint sentiment topic features using SVM and ANN,” Soft Comput. 23(16), 7067–7079, 2019, doi: 10.1007/s00500-018-3349-9.
  20.  L. Haghnegahdar and Y. Wang, “A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection,” Neural Comput. Appl. 32(13), 9427–9441, 2020, doi: 10.1007/s00521-019-04453-w.
  21.  J. Zhou, Q. Chen, J.X. Huang, Q. V. Hu, and L. He, “Position-aware hierarchical transfer model for aspect-level sentiment classification,” Inf. Sci. (Ny). 513, pp. 1–16, 2020, doi: 10.1016/j.ins.2019.11.048.
  22.  A.K. J and S. Abirami, “Aspect-based opinion ranking framework for product reviews using a Spearman’s rank correlation coefficient method,” Inf. Sci. (Ny). 460–461, pp. 23–41, 2018, doi: 10.1016/j.ins.2018.05.003.
  23.  C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn. 20, pp. 273–297, 1995, doi: 10.1109/64.163674.

Date

05.05.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.137271

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; Early Access; e137271
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