Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining (original) (raw)

References

  1. Ahmed K, El Tazi N, Hossny AH (2015) Sentiment analysis over social networks: an overview. In: 2015 IEEE international conference on systems, man, and cybernetics, IEEE, pp 2174–2179. https://doi.org/10.1109/smc.2015.380
  2. Ahmed H, Traore I, Saad S (2018) Detecting opinion spams and fake news using text classification. Secur Priv 1(1):e9. https://doi.org/10.1002/spy2.9
    Article Google Scholar
  3. Asghar MZ, Ullah A, Ahmad S, Khan A (2019) Opinion spam detection framework using hybrid classification scheme. Soft Comput. https://doi.org/10.1007/s00500-019-04107-y
    Article Google Scholar
  4. Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Lang Resour Eval 10:2200–2204
    Google Scholar
  5. Barbado R, Araque O, Iglesias CA (2019) A framework for fake review detection in online consumer electronics retailers. Inf Process Manag 56(4):1234–1244. https://doi.org/10.1016/j.indmarman.2019.08.003
    Article Google Scholar
  6. Barushka A, Hajek P (2016) Spam filtering using regularized neural networks with rectified linear units. In: Adorni G, Cagnoni S, Gori M, Maratea M (eds) Conference of the Italian association for artificial intelligence, vol 10037. Lecture notes in computer science. Springer, Cham, pp 65–75. https://doi.org/10.1007/978-3-319-49130-1_6
    Chapter Google Scholar
  7. Barushka A, Hajek P (2018) Spam filtering in social networks using regularized deep neural networks with ensemble learning. In: Iliadis L, Maglogiannis I, Plagianakos V (eds) Artificial intelligence applications and innovations. AIAI 2018, vol 519. IFIP advances in information and communication technology. Springer, Cham, pp 38–49. https://doi.org/10.1007/978-3-319-92007-8_4
    Chapter Google Scholar
  8. Barushka A, Hajek P (2018) Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks. Appl Intell 48(10):3538–3556. https://doi.org/10.1007/s10489-018-1161-y
    Article Google Scholar
  9. Barushka A, Hajek P (2019) Spam detection on social networks using cost-sensitive feature selection and ensemble-based regularized deep neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04331-5
    Article Google Scholar
  10. Barushka A, Hajek P (2019) Review spam detection using word embeddings and deep neural networks. In: MacIntyre J, Maglogiannis I, Iliadis L, Pimenidis E (eds) Artificial intelligence applications and innovations. AIAI 2019, vol 559. IFIP advances in information and communication technology. Springer, Cham, pp 340–350. https://doi.org/10.1007/978-3-030-19823-7_28
    Chapter Google Scholar
  11. Bravo-Marquez F, Mendoza M, Poblete B (2014) Meta-level sentiment models for big social data analysis. Knowl Based Syst 69:86–99. https://doi.org/10.1016/j.knosys.2014.05.016
    Article Google Scholar
  12. Bravo-Marquez F, Frank E, Mohammad SM, Pfahringer B (2016) Determining word-emotion associations from tweets by multi-label classification. In: 2016 IEEE/WIC/ACM international conference on web intelligence (WI), IEEE, pp 536–539. https://doi.org/10.1109/wi.2016.0091
  13. Brazdil PB, Soares C, Da Costa JP (2003) Ranking learning algorithms: using IBL and meta-learning on accuracy and time results. Mach Learn 50(3):251–277. https://doi.org/10.1023/A:102171390
    Article MATH Google Scholar
  14. BrightLocal (2018) Local consumer review survey 2018. https://www.brightlocal.com/research/local-consumer-review-survey/. Accessed 8 Nov 2019
  15. Chandy R, Gu H (2012) Identifying spam in the iOS app store. In: Proceedings of the 2nd joint WICOW/AIRWeb workshop on web quality, ACM, pp 56–59. https://doi.org/10.1145/2184305.2184317
  16. Chatzakou D, Vakali A (2015) Harvesting opinions and emotions from social media textual resources. IEEE Internet Comput 19(4):46–50. https://doi.org/10.1109/MIC.2015.28
    Article Google Scholar
  17. Chen W, Yeo CK, Lau CT, Lee BS (2017) A study on real-time low-quality content detection on Twitter from the users’ perspective. PLoS ONE 12(8):e0182487. https://doi.org/10.1371/journal.pone.0182487
    Article Google Scholar
  18. Crawford M, Khoshgoftaar TM, Prusa JD, Richter AN, Al Najada H (2015) Survey of review spam detection using machine learning techniques. J Big Data 2(1):1–23. https://doi.org/10.1186/s40537-015-0029-9
    Article Google Scholar
  19. Elmurngi E, Gherbi A (2017) An empirical study on detecting fake reviews using machine learning techniques. In: 7th international conference on innovative computing technology (INTECH), IEEE, pp 107–114. https://doi.org/10.1109/intech.2017.8102442
  20. Felbermayr A, Nanopoulos A (2016) The role of emotions for the perceived usefulness in online customer reviews. J Interact Mark 36:60–76. https://doi.org/10.1016/j.intmar.2016.05.004
    Article Google Scholar
  21. Floyd K, Freling R, Alhoqail S, Cho HY, Freling T (2014) How online product reviews affect retail sales: a meta-analysis. J Retail 90(2):217–232. https://doi.org/10.1016/j.jretai.2014.04.004
    Article Google Scholar
  22. Garcia L (2018) Deception on Amazon—an NLP exploration. https://medium.com/@lievgarcia/deception-on-amazon-c1e30d977cfd. Accessed 01 Sept 2019
  23. Garcia S, Fernandez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180(10):2044–2064. https://doi.org/10.1016/j.ins.2009.12.010
    Article Google Scholar
  24. Ghai R, Kumar S, Pandey AC (2019) Spam detection using rating and review processing method. In: Panigrahi B, Trivedi M, Mishra K, Tiwari S, Singh P (eds) Smart innovations in communication and computational sciences. Springer, Singapore, pp 189–198. https://doi.org/10.1007/978-981-10-8971-8_18
    Chapter Google Scholar
  25. Hajek P (2018) Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns. Neural Comput Appl 29(7):343–358. https://doi.org/10.1007/s00521-017-3194-2
    Article Google Scholar
  26. Harris C (2012) Detecting deceptive opinion spam using human computation. In: Workshops at AAAI on artificial intelligence, AAAI, pp 87–93
  27. He R, McAuley J (2016) Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th international conference on world wide web, pp 507–517. https://doi.org/10.1145/2872427.2883037
  28. Heydari A, Ali Tavakoli M, Salim N, Heydari Z (2015) Detection of review spam: a survey. Expert Syst Appl 42(7):3634–3642. https://doi.org/10.1016/j.eswa.2014.12.029
    Article Google Scholar
  29. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 168–177. https://doi.org/10.1145/1014052.1014073
  30. Hussain N, Turab Mirza H, Rasool G, Hussain I, Kaleem M (2019) Spam review detection techniques: a systematic literature review. Appl Sci 9(5):987. https://doi.org/10.3390/app9050987
    Article Google Scholar
  31. Ikeda K, Hattori G, Ono C, Asoh H, Higashino T (2013) Twitter user profiling based on text and community mining for market analysis. Knowl Based Syst 51:35–47. https://doi.org/10.1016/j.knosys.2013.06.020
    Article Google Scholar
  32. Jain G, Sharma M, Agarwal B (2018) Spam detection on social media using semantic convolutional neural network. Int J Knowl Discov Bioinform (IJKDB) 8(1):12–26. https://doi.org/10.4018/IJKDB.2018010102
    Article Google Scholar
  33. Jain G, Sharma M, Agarwal B (2019) Spam detection in social media using convolutional and long short term memory neural network. Ann Math Artif Intell 85(1):21–44. https://doi.org/10.1007/s10472-018-9612-z
    Article MATH Google Scholar
  34. Jindal N, Liu B (2007) Analyzing and detecting review spam. In: 7th IEEE international conference on data mining, ICDM 2007, IEEE, pp 547–552. https://doi.org/10.1109/icdm.2007.68
  35. Kennedy S, Walsh N, Sloka K, McCarren A, Foster J (2019) Fact or factitious? Contextualized opinion spam detection. In: Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, ACL, pp 344–350. https://doi.org/10.18653/v1/p19-2048
  36. Kiritchenko S, Zhu X, Mohammad SM (2014) Sentiment analysis of short informal texts. J Artif Intell Res 50:723–762
    Article Google Scholar
  37. Lau RY, Liao SY, Kwok RCW, Xu K, Xia Y, Li Y (2011) Text mining and probabilistic language modeling for online review spam detecting. ACM Trans Manag Inf Syst 2(4):1–30. https://doi.org/10.1145/2070710.2070716
    Article Google Scholar
  38. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, JMLR, vol 32, pp 1188–1196
  39. Li F, Huang M, Yang Y, Zhu X (2011) Learning to identify review spam. In: International joint conference on artificial intelligence (IJCAI 2011), pp 2488–2493
  40. Li J, Ott M, Cardie C, Hovy E (2014) Towards a general rule for identifying deceptive opinion spam. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, ACL, vol 1, pp 1566–1576. https://doi.org/10.3115/v1/p14-1147
  41. Li H, Chen Z, Mukherjee A, Liu B, Shao J (2015) Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns. In: 9th international AAAI conference on web and social media (ICWSM 2015), AAAI, pp 634–637
  42. Li H, Fei G, Wang S, Liu B, Shao W, Mukherjee A, Shao J (2017) Bimodal distribution and co-bursting in review spam detection. In: 26th international conference on world wide web, ACM, pp 1063–1072. https://doi.org/10.1145/3038912.3052582
  43. Li L, Qin B, Ren W, Liu T (2017) Document representation and feature combination for deceptive spam review detection. Neurocomputing 254:33–41. https://doi.org/10.1016/j.neucom.2016.10.080
    Article Google Scholar
  44. Lim EP, Nguyen VA, Jindal N, Liu B, Lauw HW (2010) Detecting product review spammers using rating behaviors. In: 19th ACM international conference on information and knowledge management, ACM, pp 939–948. https://doi.org/10.1145/1871437.1871557
  45. Lin M, Chen Q, Yan S (2014) Network in network. In: International conference on learning representations (ICLR), ICLR, pp 1–10
  46. Liu Y, Pang B (2018) A unified framework for detecting author spamicity by modeling review deviation. Expert Syst Appl 112:148–155. https://doi.org/10.1016/j.eswa.2018.06.028
    Article Google Scholar
  47. Liu Y, Pang B, Wang X (2019) Opinion spam detection by incorporating multimodal embedded representation into a probabilistic review graph. Neurocomputing 366:276–283. https://doi.org/10.1016/j.neucom.2019.08.013
    Article Google Scholar
  48. Madisetty S, Desarkar MS (2018) A neural network-based ensemble approach for spam detection in Twitter. IEEE Trans Comput Soc Syst 5(4):973–984. https://doi.org/10.1109/TCSS.2018.2878852
    Article Google Scholar
  49. Malik MSI, Hussain A (2017) Helpfulness of product reviews as a function of discrete positive and negative emotions. Comput Hum Behav 73:290–302. https://doi.org/10.1016/j.chb.2017.03.053
    Article Google Scholar
  50. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, NIPS, vol 26, pp 3111–3119
  51. Mohammad SM, Turney PD (2013) Crowdsourcing a word–emotion association lexicon. Comput Intell 29(3):436–465. https://doi.org/10.1111/j.1467-8640.2012.00460.x
    Article MathSciNet Google Scholar
  52. Mukherjee A, Venkataraman V, Liu B, Glance N (2013) What yelp fake review filter might be doing?. In: 7th international AAAI conference on weblogs and social media, AAAI, pp 409–418
  53. Nielsen FÅ (2011) A new ANEW: evaluation of a word list for sentiment analysis in microblogs. In: Proceedings of the ESWC2011 workshop on ‘making sense of microposts’: big things come in small packages, pp 93–98
  54. Ott M, Choi Y, Cardie C, Hancock JT (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, ACL, vol 1, pp 309–319
  55. Ott M, Cardie C, Hancock J (2012) Estimating the prevalence of deception in online review communities. In: 21st international conference on world wide web, ACM, pp 201–210. https://doi.org/10.1145/2187836.2187864
  56. Ott M, Cardie C, Hancock JT (2013) Negative deceptive opinion spam. In: 2013 conference of the North American chapter of the association for computational linguistics: human language technologies, ACL, pp 497–501
  57. Pandey AC, Rajpoot DS (2019) Spam review detection using spiral cuckoo search clustering method. Evol Intell 12(2):147–164. https://doi.org/10.1007/s12065-019-00204-x
    Article Google Scholar
  58. Patel NA, Patel R (2018) A survey on fake review detection using machine learning techniques. In: 2018 4th international conference on computing communication and automation (ICCCA), IEEE, pp 1–6. https://doi.org/10.1109/ccaa.2018.8777594
  59. Peng Q, Zhong M (2014) Detecting spam review through sentiment analysis. J Softw 9(8):2065–2072. https://doi.org/10.4304/jsw.9.8.2065-2072
    Article Google Scholar
  60. Rayana S, Akoglu L (2015) Collective opinion spam detection: bridging review networks and metadata. In: 21th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 985–994. https://doi.org/10.1145/2783258.2783370
  61. Ren Y, Ji D (2017) Neural networks for deceptive opinion spam detection: an empirical study. Inf Sci 385:213–224. https://doi.org/10.1016/j.ins.2017.01.015
    Article Google Scholar
  62. Rout JK, Dalmia A, Choo KKR, Bakshi S, Jena SK (2017) Revisiting semi-supervised learning for online deceptive review detection. IEEE Access 5:1319–1327. https://doi.org/10.1109/ACCESS.2017.2655032
    Article Google Scholar
  63. Rout JK, Dash AK, Ray NK (2018) A framework for fake review detection: issues and challenges. In: 2018 international conference on information technology (ICIT), IEEE, pp 7–10. https://doi.org/10.1109/icit.2018.00014
  64. Shojaee S, Murad MAA, Azman AB, Sharef NM, Nadali S (2013) Detecting deceptive reviews using lexical and syntactic features. In: 13th international conference on intelligent systems design and applications, IEEE, pp 53–58. https://doi.org/10.1109/isda.2013.6920707
  65. Sun C, Du Q, Tian G (2016) Exploiting product related review features for fake review detection. Math Probl Eng 2016:1–7. https://doi.org/10.1155/2016/4935792
    Article Google Scholar
  66. Tang X, Qian T, You Z (2019) Generating behavior features for cold-start spam review detection. In: International conference on database systems for advanced applications, Springer, Cham, pp 324–328. https://doi.org/10.1007/978-3-030-18590-9_38
  67. The Times (2018) ‘A third of TripAdvisor reviews are fake’ as cheats buy five stars. The Times September 22, 2018. https://www.thetimes.co.uk/article/hotel-and-caf-cheats-are-caught-trying-to-buy-tripadvisor-stars-027fbcwc8. Accessed 22 Jan 2019
  68. TripAdvisor Homepage. http://ir.tripadvisor.com/. Accessed 21 Jan 2019
  69. Vidanagama DU, Silva TP, Karunananda AS (2019) Deceptive consumer review detection: a survey. Artif Intell Rev. https://doi.org/10.1007/s10462-019-09697-5
    Article Google Scholar
  70. Wang G, Xie S, Liu B, Philip SY (2011) Review graph based online store review spammer detection. In: 11th international conference on data mining (ICDM 2011), IEEE, pp 1242–1247. https://doi.org/10.1109/icdm.2011.124
  71. Wang G, Li C, Wang W, Zhang Y, Shen D, Zhang X, Henao R, Carin L (2018) Joint embedding of words and labels for text classification. In: Proceedings of the 56th annual meeting of the association for computational linguistics, ACL, pp 2321–2331. https://doi.org/10.18653/v1/p18-1216
  72. Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of human language technology conference and conference on empirical methods in natural language processing, ACL, pp 347–354
  73. Xie S, Wang G, Lin S, Yu PS (2012) Review spam detection via temporal pattern discovery. In: 18th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 823–831. https://doi.org/10.1145/2339530.2339662
  74. Xue H, Wang Q, Luo B, Seo H, Li F (2019) Content-aware trust propagation toward online review spam detection. J Data Inf Qual (JDIQ) 11(3):11. https://doi.org/10.1145/3305258
    Article Google Scholar
  75. Ye J, Kumar S, Akoglu L (2016) Temporal opinion spam detection by multivariate indicative signals. In: 10th international AAAI conference on web and social media (ICWSM 2016), AAAI, pp 743–746
  76. Yilmaz CM, Durahim AO (2018) SPR2EP: a semi-supervised spam review detection framework. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 306–313. https://doi.org/10.1109/asonam.2018.8508314
  77. Zeng ZY, Lin JJ, Chen MS, Chen MH, Lan YQ, Liu JL (2019) A review structure based ensemble model for deceptive review spam. Information 10(7):243. https://doi.org/10.3390/info10070243
    Article Google Scholar

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