Skip to main content
SpringerLink
Log in

Aspect-based sentiment analysis using deep networks and stochastic optimization

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

Abstract

Sentiment analysis, also known as opinion mining, is a computational study of unstructured textual information which is used to analyze a persons attitude from a piece of text. This paper proposes an efficient method for sentiment analysis by effectively combining three procedures: (a) creating the ontologies for extraction of semantic features (b) Word2vec for conversion of processed corpus (c) convolutional neural network (CNN) for opinion mining. For CNN parameter tuning, a multi-objective function is solved for nondominant Pareto front optimal values using particle swarm optimization. Experiments show that the proposed technique outperforms other state-of-the-art techniques while yielding 88.52%, 94.30%, 85.63% and 86.03% in accuracy, precision, recall and F-measure, respectively.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Antoniou C, Doukas JA, Subrahmanyam A (2012) Sentiment and the CAPM. Available at SSRN 2101724

  2. Al-Moslmi T, Omar N, Abdullah S, Albared M (2017) Approaches to cross-domain sentiment analysis: a systematic literature review. IEEE Access 5:16173–16192

    Article  Google Scholar 

  3. Jianqiang Z, Xiaolin G (2017) Comparison research on text pre-processing methods on twitter sentiment analysis. IEEE Access 5:2870–2879

    Article  Google Scholar 

  4. Wei W, Gulla JA (2010) Sentiment learning on product reviews via sentiment ontology tree. In: Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 404–413

  5. Kontopoulos E, Berberidis C, Dergiades T, Bassiliades N (2013) Ontology-based sentiment analysis of twitter posts. Expert Syst Appl 40(10):4065–4074

    Article  Google Scholar 

  6. Ciric M, Stanimirovic A, Petrovic N, Stoimenov L (2013) Comparison of different algorithms for sentiment classification. In: 2013 11th international conference on telecommunication in modern satellite, cable and broadcasting services (TELSIKS), vol 2. IEEE, pp 567–570

  7. Bahrainian S-A, Dengel A (2013) Sentiment analysis and summarization of twitter data. In: 2013 IEEE 16th international conference on computational science and engineering (CSE). IEEE, pp 227–234

  8. Freitas LA, Vieira R (2013) Ontology based feature level opinion mining for Portuguese reviews. In: Proceedings of the 22nd international conference on World Wide Web. ACM, pp 367–370

  9. Zhao Y, Niu K, He Z, Lin J, Wang X (2013) Text sentiment analysis algorithm optimization and platform development in social network. In: 2013 sixth international symposium on computational intelligence and design (ISCID), vol 1. IEEE, pp 410–413

  10. Bakliwal A, Foster J, van der Puil J, O’Brien R, Tounsi L, Hughes M (2013) Sentiment analysis of political tweets: towards an accurate classifier. Association for Computational Linguistics, pp 49–58

  11. Sam KM, Chatwin C (2013) Ontology-based sentiment analysis model of customer reviews for electronic products. Int J e-Educ e-Bus e-Manag e-Learn 3(6):477

    Google Scholar 

  12. Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst 89:14–46

    Article  Google Scholar 

  13. Yadollahi A, Shahraki AG, Zaiane OR (2017) Current state of text sentiment analysis from opinion to emotion mining. ACM Comput Surv: CSUR 50(2):25

    Article  Google Scholar 

  14. Clavel C, Callejas Z (2016) Sentiment analysis: from opinion mining to human-agent interaction. IEEE Trans Affect Comput 7(1):74–93

    Article  Google Scholar 

  15. Dragoni M, Poria S, Cambria E (2018) Ontosenticnet: a commonsense ontology for sentiment analysis. IEEE Intell Syst 33(3):77–85

    Article  Google Scholar 

  16. Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882

  17. Stojanovski D, Strezoski G, Madjarov G, Dimitrovski I (2015) Twitter sentiment analysis using deep convolutional neural network. In: International conference on hybrid artificial intelligence systems. Springer, pp 726–737

  18. Ouyang X, Zhou P, Li CH, Liu L (2015) Sentiment analysis using convolutional neural network. In: 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing (CIT/IUCC/DASC/PICOM). IEEE, pp 2359–2364

  19. Jindal S, Singh S (2015) Image sentiment analysis using deep convolutional neural networks with domain specific fine tuning. In: 2015 international conference on information processing (ICIP). IEEE, pp 447–451

  20. Yang T, Li Y, Pan Q, Guo L (2016) Tb-CNN: joint tree-bank information for sentiment analysis using CNN. In: 2016 35th Chinese control conference (CCC). IEEE, pp 7042–7044

  21. Bouazizi M, Ohtsuki T (2016) Sentiment analysis: from binary to multi-class classification: a pattern-based approach for multi-class sentiment analysis in twitter. In: 2016 IEEE international conference on communications (ICC). IEEE, pp 1–6

  22. Abbasi A, Chen H, Salem A (2008) Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Trans Inf Syst: TOIS 26(3):12

    Article  Google Scholar 

  23. Valdivia A, Luzón MV, Herrera F (2017) Sentiment analysis in tripadvisor. IEEE Intell Syst 32(4):72–77

    Article  Google Scholar 

  24. Che W, Zhao Y, Guo H, Su Z, Liu T (2015) Sentence compression for aspect-based sentiment analysis. IEEE/ACM Trans Audio Speech Lang Process 23(12):2111–2124

    Article  Google Scholar 

  25. Bui N, Yen J, Honavar V (2016) Temporal causality analysis of sentiment change in a cancer survivor network. IEEE Trans Comput Soc Syst 3(2):75–87

    Article  Google Scholar 

  26. Wu DD, Zheng L, Olson DL (2014) A decision support approach for online stock forum sentiment analysis. IEEE Trans Syst Man Cybern Syst 44(8):1077–1087

    Article  Google Scholar 

  27. Rani S, Kumar P (2017) A sentiment analysis system to improve teaching and learning. Computer 50(5):36–43

    Article  Google Scholar 

  28. Mouthami K, Devi K.N, Bhaskaran VM (2013) Sentiment analysis and classification based on textual reviews. In: 2013 international conference on information communication and embedded systems (ICICES). IEEE, pp 271–276

  29. Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820

  30. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering, Thapar University, Patiala, 147004, India

    Ravindra Kumar, Husanbir Singh Pannu & Avleen Kaur Malhi

Authors
  1. Ravindra Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Husanbir Singh Pannu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Avleen Kaur Malhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Avleen Kaur Malhi.

Ethics declarations

Conflict of interest

There is no conflict of interest involved in this research.

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

Kumar, R., Pannu, H.S. & Malhi, A.K. Aspect-based sentiment analysis using deep networks and stochastic optimization. Neural Comput & Applic 32, 3221–3235 (2020). https://doi.org/10.1007/s00521-019-04105-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04105-z

Keywords

Search

Navigation