Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 1
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
Go to article

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.
Go to article

Authors and Affiliations

Nallathambi Balaganesh
1
ORCID: ORCID
K. Muneeswaran
1
ORCID: ORCID

  1. Department of Computer Science & Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamilnadu, India

This page uses 'cookies'. Learn more