Skip to main content
Log in

Intelligent fake reviews detection based on aspect extraction and analysis using deep learning

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

Abstract

In the era of social networking and e-commerce sites, users provide their feedback and comments in the form of reviews for any product, topic, or organization. Due to high influence of reviews on users, spammers use fake reviews to promote their product/organization and to demote the competitors. It is estimated that approximately 14% of reviews on any platform are fake reviews. Several researchers have proposed various approaches to detect fake reviews. The limitation of existing approaches is that complete review text is analysed which increases computation time and degrades accuracy. In our proposed approach, aspects are extracted from reviews and only these aspects and respective sentiments are employed for fake reviews detection. Extracted aspects are fed into CNN for aspect replication learning. The replicated aspects are fed into LSTM for fake reviews detection. As per our knowledge, aspects extraction and replication are not applied for fake reviews detection which is our significant contribution due to optimization it offers. Ott and Yelp Filter datasets are used to compare performance with recent approaches. Experiment analysis proves that our proposed approach outperforms recent approaches. Our approach is also compared with traditional machine learning techniques to prove that deep neural networks perform complex computation better than traditional techniques.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. https://www.kaggle.com/datasets/rtatman/deceptive-opinion-spam-corpus.

  2. https://www.yelp.com/.

References

  1. Cambria E, Schuller B, Liu B, Wang H, Havasi C (2013) Statistical approaches to concept-level sentiment analysis. IEEE Intell Syst 28(3):6–9

    Article  Google Scholar 

  2. Bhuvaneshwari P, Nagaraja Rao A, Harold Robinson Y (2021) Spam review detection using self attention based CNN and bi-directional LSTM. Multimedia Tools Appl 1–18

  3. Heydari A, Ma T, Salim N, Heydari Z (2015) Detection of review spam. Exp Syst Appl Int J 42(7):3634–3642

    Article  Google Scholar 

  4. Li J, Pin L, Xiao W, Yang L, Zhang P (2021) Exploring groups of opinion spam using sentiment analysis guided by nominated topics. Expert Syst Appl 171

  5. Filieri R, Alguezaui S, McLeay F (2015) Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tourism Manag 51(C):174–185

    Article  Google Scholar 

  6. Dellarocas C (2006) Strategic manipulation of internet opinion forums: implications for consumers and firms. Manag Sci 52(10):1577–1593

    Article  Google Scholar 

  7. Ott M, Cardie C, Hancock J (2012) Estimating the prevalence of deception in online review communities. In: Proceedings of the 21st international conference on world wide web, Lyon, France

  8. Wu Y, Ngai EW, Wu P, Wu C (2020) Fake online reviews: literature review, synthesis, and directions for future research. Decis Support Syst 132:113280

    Article  Google Scholar 

  9. Sandulescu V, Ester M (2015) Detecting singleton review spammers using semantic similarity. In: WWW'15 Companion: proceedings of the 24th international conference on World Wide Web, Florence, Italy

  10. Jindal N, Liu B (2007) Review spam detection. In: Proceedings of the 16th international conference on World Wide Web, Alberta, Canada

  11. Ruan N, Deng R, Su C (2020) GADM: manual fake review detection for O2O commercial platforms. Comput Secur 88:101657

    Article  Google Scholar 

  12. Ma Y, Peng H, Khan T, Cambria E, Hussain A (2018) Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cogn Comput 10(4):639–650

    Article  Google Scholar 

  13. Poria S, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl-Based Syst 108:42–49

    Article  Google Scholar 

  14. Liang B, Su H, Gui L, Cambria E, Xu R (2022) Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowledge-Based Syst 235:107643

    Article  Google Scholar 

  15. Rana T, Cheah Y (2016) Aspect extraction in sentiment analysis: comparative analysis and survey. Artif Intell Rev 46(4):459–483

    Article  Google Scholar 

  16. Poria S, Cambria E, Ku L-W, Gui C, Gelbukh A (2014) A rule-based approach to aspect extraction from product reviews. In: Proceedings of the 2nd workshop on natural language processing for social media (SocialNLP), Dublin, Ireland

  17. Liu K, Xu L, Zhao J (2012) Opinion target extraction using word-based translation model. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, Jeju Island, Korea

  18. Popescu A-M, Etzioni O (2007) Extracting product features and opinions from reviews. In: Kao A, Poteet SR (eds) Natural language processing and text mining. Springer, London, pp 9–28

    Chapter  Google Scholar 

  19. Do HH, Prasad PW, Maag A, Alsadoon A (2019) Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst Appl 118:272–299

    Article  Google Scholar 

  20. Bathla G, Singh P, Kumar S, Verma M, Garg D, Kotecha K (2021) Recop: fine-grained opinions and sentiments-based recommender system for industry 5.0. Soft Comput 1–10

  21. Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Lrec 10(2010)

  22. Cambria E, Liu Q, Decherchi S, Xing F, Kwok K (2022) SenticNet 7: a commonsense-based neurosymbolic AI framework for explainable sentiment analysis. LREC

  23. Li J, Cardie C, Li S (2013) Topicspam: a topic-model based approach for spam detection. In: Proceedings of the 51st annual meeting of the association for computational linguistics, vol 2: Short Papers, Sofia, Bulgaria

  24. Shahariar GM, Biswas S, Omar F, Shah FM, Hassan SB (2019) Spam review detection using deep learning. In: 10th Annual information technology, electronics and mobile communication conference (IEMCON), Vancouver, BC, Canada

  25. Ott M, Choi Y, Cardie C, Hancock J (2011) Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, Portland, Oregon, USA

  26. Mukherjee A, Venkataraman V, Liu B, Glance N (2013) What yelp fake review filter might be doing. In: Proceedings of the international AAAI conference on weblogs and social media (ICWSM-2013), Massachusetts, USA

  27. Ren Y, Ji D (2017) Neural networks for deceptive opinion spam detection: an empirical study. Inf Sci 385–386:213–224

    Article  Google Scholar 

  28. Fahfouh A, Riffi J, Mahraz MA, Yahyaouy A, Tairi H (2020) PV-DAE: a hybrid model for deceptive opinion spam based on neural network architectures. Expert Syst Appl 157:113517

    Article  Google Scholar 

  29. Zhong M, Tan L, Qu X (2020) Identification of opinion spammers using reviewer reputation and clustering analysis. Int J Comput Commun Control 14(6):759–772

    Article  Google Scholar 

  30. Anass F, Jamal R, Mahraz MA, Ali Y, Tairi H (2020) Deceptive opinion spam based on deep learning. In: 4th International conference on intelligent computing in data sciences (ICDS), Morocco

  31. Javed MS, Majeed H, Mujtaba H, Beg MO (2021) Fake reviews classification using deep learning ensemble of shallow convolutions. J Comput Soc Sci 4(2):883–902

    Article  Google Scholar 

  32. Tian Y, Mirzabagheri M, Tirandazi P, Mojtaba Hosseini Bamakan S (2020) A non-convex semi-supervised approach to opinion spam detection by ramp-one class SVM. Inf Process Manag 57(6)

  33. Noekhah S, Salim N, Zakaria NH (2020) Opinion spam detection: using multi-iterative graph-based model. Inf Process Manag 57(1):102140

    Article  Google Scholar 

  34. Dong M, Yao L, Wang X, Benatallah B, Huang C, Ning X (2020) Opinion fraud detection via neural autoencoder decision forest. Pattern Recogn Lett 132:21–29

    Article  Google Scholar 

  35. Budhi GS, Chiong R, Wang Z, Dhakal S (2021) Using a hybrid content-based and behaviour-based featuring approach in a parallel environment to detect fake reviews. Electron Commer Res Appl 47:101048

    Article  Google Scholar 

  36. Liu P, Zhenning X, Jun A, Wang F (2017) Identifying indicators of fake reviews based on spammer's behavior features. In: IEEE international conference on software quality, reliability and security

  37. Yuan C, Zhou W, Ma Q, Lv S, Han J, Hu S (2019) Learning review representations from user and product level information for spam detection. In: International conference on data mining (ICDM), Beijing, 2019.

  38. Hu N, Bose I, Koh NS, Liu L (2012) Manipulation of online reviews: an analysis of ratings, readability, and sentiments. Decis Support Syst 52(3):674–684

    Article  Google Scholar 

  39. Yin C, Cuan H, Zhu Y, Yin Z (2021) Improved fake reviews detection model based on vertical ensemble tri-training and active learning. ACM Trans. Intell. Syst. Technol. (TIST) 12(4):1–19

    Google Scholar 

  40. Singh RK, Singh P, Bathla G (2020) User-review oriented social recommender system for event planning. Ingénierie des Systèmes d’Information 25(5):669–675

    Article  Google Scholar 

  41. Gong M, Gao Y, Xie Y, Qin AK (2020) An attention-based unsupervised adversarial model for movie review spam detection. IEEE Trans Multimedia.

  42. Wang G, Xie S, Liu B, Yu PS (2011) Review graph based online store review spammer detection. In: 11th international conference on data mining, Vancouver, Canada

  43. Panda A, Yadlapalli B, Zhou Z (2021) Credit card fraud detection through machine learning algorithm. Big Data Comput Vis 1(3):140–145. https://doi.org/10.22105/bdcv.2021.142231

    Article  Google Scholar 

  44. Collobert R, Weston J, Bottou L, Karlen MK, Kavukcuoglu KP (2011) Natural language processing (Almost) from scratch. J Mach Learn Res 12(1):2493–2537

    MATH  Google Scholar 

  45. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Doha, Qatar

  46. Khodaverdian Z, Sadr H, Edalatpanah SA, Solimandarabi MN (2021) Combination of convolutional neural network and gated recurrent unit for energy aware resource allocation. arXiv preprint arXiv:2106.12178

  47. Khodaverdian Z, Sadr H, Edalatpanah SA (2021) A shallow deep neural network for selection of migration candidate virtual machines to reduce energy consumption. In: 7th international conference on web research (ICWR)

  48. Chen C, Zhao H, Yang Y (2015) Deceptive opinion spam detection using deep level linguistic features. In: Natural language processing and Chinese computing, Nanchang, China

  49. Jia S, Zhang X, Wang X, Liu Y (2018) Fake reviews detection based on LDA. In: 4th international conference on information management (ICIM), Oxford, UK

  50. Khurshid F, Zhu Y, Xu Z, Ahmad M, Ahmad M (2019) Enactment of ensemble learning for review spam detection on selected features. Int J Comput Intell Syst 12(1):387–394

    Article  Google Scholar 

  51. Singh RK, Sachan MK, Patel RB (2021) 360 degree view of cross-domain opinion classification: a survey. Artif Intell Rev 54(2):1385–1506

    Article  Google Scholar 

  52. Rasheed F, Wahid A (2021) Learning style detection in E-learning systems using machine learning techniques. Expert Syst Appl 174:114774

    Article  Google Scholar 

  53. Salminen J, Kandpal C, Kamel AM, Jung SG, Jansen BJ (2022) Creating and detecting fake reviews of online products. J Retail Consum Serv 64:102771

    Article  Google Scholar 

  54. Shahariar GM, Biswas S, Omar F, Shah FM, Hassan SB (2019) Spam review detection using deep learning, In: IEEE 10th annual information technology, electronics and mobile communication conference (IEMCON), pp 0027–0033

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rahul Kumar Singh.

Ethics declarations

Conflict of interest

The authors declare that they have 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

Bathla, G., Singh, P., Singh, R.K. et al. Intelligent fake reviews detection based on aspect extraction and analysis using deep learning. Neural Comput & Applic 34, 20213–20229 (2022). https://doi.org/10.1007/s00521-022-07531-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07531-8

Keywords

Navigation