Skip to main content
Top
Published in: International Journal of Machine Learning and Cybernetics 8/2019

16-12-2017 | Original Article

Spam analysis of big reviews dataset using Fuzzy Ranking Evaluation Algorithm and Hadoop

Authors: Komal Dhingra, Sumit Kr Yadav

Published in: International Journal of Machine Learning and Cybernetics | Issue 8/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Online reviews are the most easily available free information sources used by both organizations and customers to make decisions. Establishments are utilizing significance of opinions to earn undue profit by hiring professionals known as spammers, giving positive comments on their products and negative opinions on their competitor’s product. This activity is known as opinion spamming and should be identified to give genuine results containing sentiments towards a product. So far, opinion spam detection has been considered as a discrete classification problem, generally as spam and non-spam. However, it involves uncertainty as suspicious behavior of a user might be due to coincidence. As, fuzzy logic handles real world uncertainty very well, we propose a novel fuzzy modeling based solution to the problem. We have proposed four fuzzy input linguistic variable and considered suspicious level of a spammer group to be one of—Ultra, Mega, Immense, Highly, Moderate, Slightly and Feebly. We have defined novel FSL Deduction Algorithm generating 81 fuzzy rules and Fuzzy Ranking Evaluation Algorithm (FREA) to determine the extent to which a group is suspicious. As reviews dataset satisfy the three V’s of big data (Volume, Velocity and Variety), we have considered this problem as a big data problem and used Hadoop for storage and analyzation. We have further demonstrated our proposed algorithm using a sample reviews dataset and Amazon reviews dataset achieving an accuracy of 80.77% which unlike other approaches remains steady for large number of groups and deals well with uncertainty involved in opinion spam detection.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Show more products
Literature
2.
go back to reference Adike MR, Reddy V (2016) Detection of fake review and brand spam using data mining. Int J Recent Trends Eng Res 2(7):251–256 Adike MR, Reddy V (2016) Detection of fake review and brand spam using data mining. Int J Recent Trends Eng Res 2(7):251–256
4.
go back to reference Ahuja Y, Yadav SK (2012) Multiclass classification and support vector machine. Global J Comput Sci Technol Interdiscip 12(11):14–20 Ahuja Y, Yadav SK (2012) Multiclass classification and support vector machine. Global J Comput Sci Technol Interdiscip 12(11):14–20
5.
go back to reference Akoglu L, Chandy R, Faloutsos C (2013) Opinion fraud detection in online reviews by network effects. In: Seventh international AAAI conference on weblogs and social media vol 13. AAAI Publications, pp 2–11 Akoglu L, Chandy R, Faloutsos C (2013) Opinion fraud detection in online reviews by network effects. In: Seventh international AAAI conference on weblogs and social media vol 13. AAAI Publications, pp 2–11
6.
go back to reference Al-Anzi FS, Yadav SK, Soni J (2014) Cloud computing: security model comprising governance, risk management and compliance. In: International conference on data mining and intelligent computing (ICDMIC). IEEE, pp. 1–6 Al-Anzi FS, Yadav SK, Soni J (2014) Cloud computing: security model comprising governance, risk management and compliance. In: International conference on data mining and intelligent computing (ICDMIC). IEEE, pp. 1–6
7.
go back to reference Andrea E, Sebastiani F (2006) Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of the 5th conference on language resources and evaluation (LREC 2006), vol. 6, pp. 417–422 Andrea E, Sebastiani F (2006) Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of the 5th conference on language resources and evaluation (LREC 2006), vol. 6, pp. 417–422
9.
go back to reference Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC vol 10. European Language Resources Association, pp 2200–2204 Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC vol 10. European Language Resources Association, pp 2200–2204
12.
go back to reference Bhushan M, Banerjea S, Yadav SK (2014) Bloom filter based optimization on HBase with MapReduce. In: 2014 International conference on data mining and intelligent computing (ICDMIC). IEEE, pp. 1–5 Bhushan M, Banerjea S, Yadav SK (2014) Bloom filter based optimization on HBase with MapReduce. In: 2014 International conference on data mining and intelligent computing (ICDMIC). IEEE, pp. 1–5
16.
go back to reference Choo E, Yu T, Chi M (2015) Detecting opinion spammer groups through community discovery and sentiment analysis. In: Samarati P (ed) Data and applications security and privacy XXIX. DBSec 2015. Lecture Notes Computer Science vol 9149. Springer, Cham, pp 170–187. https://doi.org/10.1007/978-3-319-20810-7_11 Choo E, Yu T, Chi M (2015) Detecting opinion spammer groups through community discovery and sentiment analysis. In: Samarati P (ed) Data and applications security and privacy XXIX. DBSec 2015. Lecture Notes Computer Science vol 9149. Springer, Cham, pp 170–187. https://​doi.​org/​10.​1007/​978-3-319-20810-7_​11
19.
go back to reference DeRoos D, Zikopoulos P, Brown B, Coss R, Melnyk RB (2014) Hadoop for dummies. Wiley, Hoboken DeRoos D, Zikopoulos P, Brown B, Coss R, Melnyk RB (2014) Hadoop for dummies. Wiley, Hoboken
20.
go back to reference Dixit S, Agrawal AJ (2013) Survey on review spam detection. Int J Comput Commun Technol 4(2):68–72 Dixit S, Agrawal AJ (2013) Survey on review spam detection. Int J Comput Commun Technol 4(2):68–72
28.
go back to reference Hu X, Tang J, Zhang Y, Liu H (2013) Social spammer detection in microblogging. In: Proceedings of the twenty-third international joint conference on artificial intelligence (IJCAI), vol. 13, pp. 2633–2639 Hu X, Tang J, Zhang Y, Liu H (2013) Social spammer detection in microblogging. In: Proceedings of the twenty-third international joint conference on artificial intelligence (IJCAI), vol. 13, pp. 2633–2639
32.
34.
go back to reference 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: International AAAI conference on web and social media. AAAI Press, California pp 634–637 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: International AAAI conference on web and social media. AAAI Press, California pp 634–637
36.
go back to reference Li J, Ott M, Cardie C, Hovy EH (2014) Towards a general rule for identifying deceptive opinion spam. In: Proceedings of the 52nd annual meeting of the association for computational linguistics. ACL, Baltimore, pp. 1566–1576 Li J, Ott M, Cardie C, Hovy EH (2014) Towards a general rule for identifying deceptive opinion spam. In: Proceedings of the 52nd annual meeting of the association for computational linguistics. ACL, Baltimore, pp. 1566–1576
37.
go back to reference Li L, Ren W, Qin B, Liu T (2015) Learning document representation for deceptive opinion spam detection. In: Sun M, Liu Z, Zhang M, Liu Y (eds) Chinese computational linguistics and natural language processing based on naturally annotated big data. Lecture NotesComputer Science vol 9427. Springer, Cham, pp 393–403. https://doi.org/10.1007/978-3-319-25816-4_32 CrossRef Li L, Ren W, Qin B, Liu T (2015) Learning document representation for deceptive opinion spam detection. In: Sun M, Liu Z, Zhang M, Liu Y (eds) Chinese computational linguistics and natural language processing based on naturally annotated big data. Lecture NotesComputer Science vol 9427. Springer, Cham, pp 393–403. https://​doi.​org/​10.​1007/​978-3-319-25816-4_​32 CrossRef
39.
42.
go back to reference McAuley J, Pandey R, Leskovec J (2015) Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD International conference on knowledge discovery and data mining. ACM, pp. 785–794 McAuley J, Pandey R, Leskovec J (2015) Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD International conference on knowledge discovery and data mining. ACM, pp. 785–794
43.
go back to reference McAuley J, Targett C, Shi Q, Hengel AVD (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, pp. 43–52 McAuley J, Targett C, Shi Q, Hengel AVD (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, pp. 43–52
45.
go back to reference Nadaf SB, Gujar AD (2016) A survey paper on spam mail detection using RFD. Int J Adv Res Comput Sci Manag Stud 4(1):46–48 Nadaf SB, Gujar AD (2016) A survey paper on spam mail detection using RFD. Int J Adv Res Comput Sci Manag Stud 4(1):46–48
46.
go back to reference Nandimath JN, Katkar BS, Ghadge VU, Garad AN (2017) Efficiently detecting and analyzing spam reviews using live data feed. Int Res J Eng Technol (IRJET) 4(2):1421–1424 Nandimath JN, Katkar BS, Ghadge VU, Garad AN (2017) Efficiently detecting and analyzing spam reviews using live data feed. Int Res J Eng Technol (IRJET) 4(2):1421–1424
51.
go back to reference Ott M, Cardie C, Hancock JT (2013) Negative deceptive opinion spam. In: Proceedings of North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) 2013. Association for Computational Linguistics, pp. 497–501 Ott M, Cardie C, Hancock JT (2013) Negative deceptive opinion spam. In: Proceedings of North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) 2013. Association for Computational Linguistics, pp. 497–501
52.
go back to reference 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, vol.1. Association for Computational Linguistics, pp. 309–319 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, vol.1. Association for Computational Linguistics, pp. 309–319
54.
go back to reference Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol. 10. Association for Computational Linguistics, pp. 79–86. https://doi.org/10.3115/1118693.1118704 Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol. 10. Association for Computational Linguistics, pp. 79–86. https://​doi.​org/​10.​3115/​1118693.​1118704
57.
go back to reference Qian T, Liu B (2013) Identifying multiple userids of the same author. In: Proceedings of conference on empirical methods in natural language processing (EMNLP-2013), pp. 1124–1135 Qian T, Liu B (2013) Identifying multiple userids of the same author. In: Proceedings of conference on empirical methods in natural language processing (EMNLP-2013), pp. 1124–1135
59.
go back to reference Rao Y, Xie H, Li J, Jin F, Wang FL, Li Q (2016) Social emotion classification of short text via topic-level maximum entropy model. Inf Manag 53(8):978–986CrossRef Rao Y, Xie H, Li J, Jin F, Wang FL, Li Q (2016) Social emotion classification of short text via topic-level maximum entropy model. Inf Manag 53(8):978–986CrossRef
61.
go back to reference Roul RK, Asthana SR, Kumar G (2016) Spam web page detection using combined content and link features. Int J Data Mining Model Manag 8(3):209–222 Roul RK, Asthana SR, Kumar G (2016) Spam web page detection using combined content and link features. Int J Data Mining Model Manag 8(3):209–222
63.
go back to reference Rubin VL (2017) Deception detection and rumor debunking for social media. In: Sloan L, Quan-Haase(eds) A handbook of social media research methods. Sage, London, pp 1–25 Rubin VL (2017) Deception detection and rumor debunking for social media. In: Sloan L, Quan-Haase(eds) A handbook of social media research methods. Sage, London, pp 1–25
67.
go back to reference Tavakoli M, Heydari A, Ismail Z, Salim N (2015) A framework for review spam detection research. World Academy of Science, Engineering and Technology. Int J Comput Electrical Autom Control Inf Eng 10(1):67–71 Tavakoli M, Heydari A, Ismail Z, Salim N (2015) A framework for review spam detection research. World Academy of Science, Engineering and Technology. Int J Comput Electrical Autom Control Inf Eng 10(1):67–71
68.
go back to reference Tayal DK, Yadav SK (2015) Word level sentiment analysis using fuzzy sets. Int J Adv Sci Technol. 54: 73–78 Tayal DK, Yadav SK (2015) Word level sentiment analysis using fuzzy sets. Int J Adv Sci Technol. 54: 73–78
69.
go back to reference Tayal DK, Yadav SK (2016) Fast retrieval approach of sentimental analysis with implementation of bloom filter on Hadoop. In: 2016 International conference on computational techniques in information and communication technologies (ICCTICT). IEEE, pp. 14–18. https://doi.org/10.1109/ICCTICT.2016.7514544 Tayal DK, Yadav SK (2016) Fast retrieval approach of sentimental analysis with implementation of bloom filter on Hadoop. In: 2016 International conference on computational techniques in information and communication technologies (ICCTICT). IEEE, pp. 14–18. https://​doi.​org/​10.​1109/​ICCTICT.​2016.​7514544
73.
go back to reference Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Predicting elections with twitter: What 140 characters reveal about political sentiment. In: Proceedings of the fourth international aaai conference on weblogs and social media(ICWSM), vol. 10, no. 1, pp. 178–185 Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Predicting elections with twitter: What 140 characters reveal about political sentiment. In: Proceedings of the fourth international aaai conference on weblogs and social media(ICWSM), vol. 10, no. 1, pp. 178–185
77.
79.
go back to reference Yadav SK, Bhushan M, Gupta S (2015) Multimodal sentiment analysis: Sentiment analysis using audiovisual format. In: 2015 2nd international conference on computing for Sustainable Global Development (INDIACom). IEEE, pp. 1415–1419 Yadav SK, Bhushan M, Gupta S (2015) Multimodal sentiment analysis: Sentiment analysis using audiovisual format. In: 2015 2nd international conference on computing for Sustainable Global Development (INDIACom). IEEE, pp. 1415–1419
80.
go back to reference Yadav S, Dhingra K, Kaushik D (2016) Opinion mining using SentiFul. In: 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, pp. 2406–2411 Yadav S, Dhingra K, Kaushik D (2016) Opinion mining using SentiFul. In: 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, pp. 2406–2411
81.
go back to reference Ye J, Kumar S, Akoglu L (2016) Temporal opinion spam detection by multivariate indicative signals. In: Proceedings of the tenth international AAAI conference on web and social media. Association for the Advancement of Artificial Intelligence, pp. 743–746 Ye J, Kumar S, Akoglu L (2016) Temporal opinion spam detection by multivariate indicative signals. In: Proceedings of the tenth international AAAI conference on web and social media. Association for the Advancement of Artificial Intelligence, pp. 743–746
82.
go back to reference Yen J, Langari R (1998) Fuzzy logic: intelligence, control, and information. Prentice-Hall, Inc., Upper Saddle River Yen J, Langari R (1998) Fuzzy logic: intelligence, control, and information. Prentice-Hall, Inc., Upper Saddle River
Metadata
Title
Spam analysis of big reviews dataset using Fuzzy Ranking Evaluation Algorithm and Hadoop
Authors
Komal Dhingra
Sumit Kr Yadav
Publication date
16-12-2017
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 8/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0768-3

Other articles of this Issue 8/2019

International Journal of Machine Learning and Cybernetics 8/2019 Go to the issue