Skip to main content
Log in

A novel aspect-based sentiment classifier using whale optimized adaptive neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In the field of e-commerce applications, nowadays the aspect-based sentiment analysis has become vital and every consumer started focusing on various aspects of the product before making the purchase decision through online portals like Amazon, Walmart, Alibaba, Flipkart, etc. Hence, the enhancement of sentiment classification considering every aspect of product and services is in the limelight. In this proposed research, aspect-based sentiment classification model has been developed employing sentiment whale optimized adaptive neural network (SWOANN) for classifying the sentiment of key aspects of products and services. The proposed work uses the key features such as the positive opinion score, negative opinion score and the term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. Moreover, the computational speed and accuracy of sentiment classification of the product and services have been improved by the optimal selection of weights of the neurons of proposed model. The promising results are obtained by analysing 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 model can be compatible with any sentiment classification problem of products and services.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Afzaal M, Usman M, Fong A (2019) Tourism mobile app with aspect-based sentiment classification framework for tourist reviews. IEEE Trans Consum Electron 65(2):233–242. https://doi.org/10.1109/TCE.2019.2908944

    Article  Google Scholar 

  2. Akhtar MS, Garg T, Ekbal A (2020) Multi-task learning for aspect term extraction and aspect sentiment classification. Neurocomputing 398:247–256. https://doi.org/10.1016/j.neucom.2020.02.093

    Article  Google Scholar 

  3. Al-Smadi M, Qawasmeh O, Al-Ayyoub M, Jararweh Y, Gupta B (2018) Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. J Comput Sci 27:386–393. https://doi.org/10.1016/j.jocs.2017.11.006

    Article  Google Scholar 

  4. Alharbi JR, Alhalabi WS (2020) 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. https://doi.org/10.4018/IJSWIS.2020010106

    Article  Google Scholar 

  5. Brychcín T, Konkol M, & Steinberger J (2015) UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis. Proc. 8th Int. Workshop Semantic Eval. (SemEval) (2014), SemEval, 817–822. https://doi.org/10.3115/v1/s14-2145

  6. Cortes C, Vapnik V (1995) Support-Vector Networks. Mach Learn 20:273–297. https://doi.org/10.1109/64.163674

    Article  MATH  Google Scholar 

  7. Cui L, Huang S, Wei F, Tan C, Duan C, & Zhou M (2017) Superagent: A customer service chatbot for E-commerce websites. ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations, 97–102. https://doi.org/10.18653/v1/P17-4017

  8. Ghiassi M, Skinner J, Zimbra D (2013) Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst Appl 40(16):6266–6282. https://doi.org/10.1016/j.eswa.2013.05.057

    Article  Google Scholar 

  9. Haghnegahdar L, Wang Y (2020) A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection. Neural Comput Appl 32(13):9427–9441. https://doi.org/10.1007/s00521-019-04453-w

    Article  Google Scholar 

  10. Iqbal F, Hashmi JM, Fung BCM, Batool R, Khattak AM, Aleem S, Hung PCK (2019) A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction. IEEE Access 7:14637–14652. https://doi.org/10.1109/ACCESS.2019.2892852

    Article  Google Scholar 

  11. J, A. K., & Abirami, S. (2018) Aspect-based opinion ranking framework for product reviews using a Spearman’s rank correlation coefficient method. Inf Sci 460–461:23–41. https://doi.org/10.1016/j.ins.2018.05.003

    Article  Google Scholar 

  12. Kai Y, Cai Y, Dongping H, Li J, Zhou Z, Lei X (2017) An effective hybrid model for opinion mining and sentiment analysis. IEEE Int Conf Big Data Smart Comput BigComp 2017:465–466. https://doi.org/10.1109/BIGCOMP.2017.7881759

    Article  Google Scholar 

  13. Kalarani P, Selva Brunda S (2019) Sentiment analysis by POS and joint sentiment topic features using SVM and ANN. Soft Comput 23(16):7067–7079. https://doi.org/10.1007/s00500-018-3349-9

    Article  Google Scholar 

  14. Mirjalili S, Lewis A (2016) The Whale Optimization Algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  15. Mladenović M, Mitrović J, Krstev C, Vitas D (2016) Hybrid sentiment analysis framework for a morphologically rich language. J Intell Inf Syst 46(3):599–620. https://doi.org/10.1007/s10844-015-0372-5

    Article  Google Scholar 

  16. Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, & Manandhar S (2014) SemEval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings Ofthe 8th International Workshop on Semantic Evaluation (SemEval 2014), 27–35. https://doi.org/10.3115/v1/s14-2004

  17. Singh J, Singh G, Singh R (2017) Optimization of sentiment analysis using machine learning classifiers. Human-Centric Comput Inf Sci. https://doi.org/10.1186/s13673-017-0116-3

    Article  Google Scholar 

  18. Zhou J, Chen Q, Huang JX, Hu QV, He L (2020) Position-aware hierarchical transfer model for aspect-level sentiment classification. Inf Sci 513:1–16. https://doi.org/10.1016/j.ins.2019.11.048

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their sincere thanks and gratitude to the Management and Principal of Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India, for supporting us with State-of-the-art facilities in collaboration with Anna University Chennai, Tamil Nadu, to carry out our research work at the Mepco Research Centre.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Balaganesh.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balaganesh, N., Muneeswaran, K. A novel aspect-based sentiment classifier using whale optimized adaptive neural network. Neural Comput & Applic 34, 4003–4012 (2022). https://doi.org/10.1007/s00521-021-06660-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06660-w

Keywords

Navigation