Due to proliferation of competitive online Business-to-Consumer (B2C) models, it is becoming a challenging task for new users to choose best products, based on existing users’ reviews residing on different e-commerce websites. On analysis, it is found that the opinions of the existing customers play an important role for new customers in making appropriate purchase decisions. Though there are some online websites that provide aggregation of basic product information from multiple sources, there is a negligible research effort in the direction of opinion-based product ranking. In this paper, we propose an Opinion-based Multi-Criteria Ranking (OMCR) approach, which amalgamates structural and content-based features of review documents to rank different alternatives of the online products. It uses a total number of five features based on reviews’ meta-data and contents to rank different alternatives using multi-criteria decision making approaches. OMCR also incorporates a sentiment analysis and visualization approach to determine sentiment polarity values and visualize them in a comprehendible manner. Experiments are conducted over two different real datasets, and efficacy of OMCR is assessed using set intersection method, which is generally used to compare two ranked lists in terms of their overlapping score.
V.Belton and T.Gear, On a short-coming of saaty’s method of analytic hierarchies, Omega11(3) (1983), 228–230.
2.
E.Cano and M.Morisio, Hybrid recommender systems: A systematic literature review, Intelligent Data Analysis21(6) (2017), 1487–1524.
3.
M.Chae and J.Kim, Do size and structure matter to mobile users? an empirical study of the effects of screen size, information structure, and task complexity on user activities with standard web phones, Behaviour & Information Technology23(3) (2004), 165–181.
4.
C.H.Chen, L.P.Khoo and W.Yan, Evaluation of multicultural factors from elicited customer requirements for new product development, Research in Engineering Design14(3) (2003), 119–130.
5.
Y.Chou, C.Lee and J.Chung, Understanding m-commerce payment systems through the analytic hierarchy process, Journal of Business Research57(12) (2004), 1423–1430.
6.
M.C.Chuang, C.C.Chang and S.H.Hsu, Perceptual factors underlying user preferences toward product form of mobile phones, International Journal of Industrial Ergonomics27(4) (2001), 247–258.
7.
A.Denguir-Rekik, J.Montmain and G.Mauris, A possibilisticvalued multi-criteria decision-making support for marketing activities in e-commerce: Feedback based diagnosis system, European Journal of Operational Research195(2) (2009), 876–888.
8.
S.H.Han and S.W.Hong, A systematic approach for coupling user satisfaction with product design, Ergonomics46(13-14) (2003), 1441–1461.
9.
Y.-H.Hu, Y.-L.Chen and H.-L.Chou, Opinion mining from online hotel reviews a text summarization approach, Information Processing & Management53(2) (2017), 436–449.
10.
S.H.Han, K.J.Kim, M.H.Yun, S.W.Hong and J.Kim, Identifying mobile phone design features critical to user satisfaction, Human Factors in Ergonomics & Manufacturing14(1) (2004), 15–29.
11.
H.-Y.Hsieh and S.-H.Wu, Ranking online customer reviews with the SVR model, in: Proceedings of the 2015 IEEE 16th International Conference on Information Reuse and Integration, IEEE,2015, pp. 550–555.
12.
C.L.Hwang and K.Yoon, Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey,1st ed., Springer-Verlag, BerlinHeidelberg, 1981.
13.
G.Isiklar and G.Büyükózkan, Using a multi-criteria decision making approach to evaluate mobile phone alternatives, Computer Standards & Interfaces29(2) (2007), 265–274.
14.
A.Kamal, M.Abulaish and T.Anwar, Mining feature-opinion pairs and their reliability scores from web opinion sources, in: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, WIMS'12, ACM, Craiova, Romania, 2012, pp. 15:1–15:7.
15.
E.Kujawski, Multi-Criteria Decision Analysis: Limitations, Pitfalls, and Practical Difficulties, Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, Calif., URL: https://escholarship.org/uc/item/0cp6j7sj, 2003, pp. 1–8.
16.
K.Lakiotaki, N.F.Matsatsinis and A.Tsoukias, Multicrite-ria user modeling inrecommender systems, IEEE Intelligent Systems26(2) (2011), 64–76.
17.
Y.S.Lee, S.W.Hong, T.L.Smith-Jackson, M.A.Nussbaum and K.Tomioka, Systematic evaluation methodology for cell phone user interfaces, Interacting with Computers18(2) (2006), 304–325.
18.
M.Makrehchi and M.S.Kamel, Feature ranking fusion for text classifier, Intelligent Data Analysis16(6) (2012), 879–896.
19.
L.Mookiah, W.Eberle and M.Mondal, Personalized news recommendation using graph-based approach, Intelligent Data Analysis22(4) (2018), 881–909.
20.
J.-C.Pomerol and S.Barba-Romero, Multicriterion Decision in Management: Principles and Practice, vol. 25, 1st ed., SpringerUS, 2000.
21.
R.W.Saaty, The analytic hierarchy process-what it is and how it is used, Mathematical Modelling9(3-5) (1987), 161–176.
22.
T.L.Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, 1st ed., McGraw-Hill, New York, 1980.
23.
J.L.Salmeron and I.Herrero, An AHP-based methodology to rank critical success factors of executive information systems, Computer Standard & Interfaces28(1) (2005), 1–12.
24.
M.C.Y.Tam and V.M.R.Tummala, An application of the AHP in vendor selection of a telecommunications system, Omega29(2) (2001), 171–182.
25.
M.Velasquez and P.T.Hester, An analysis of multi-criteria decision making methods, International Journal of Operations Research10(2) (2013), 56–66.
26.
W.Webber, A.Moffat and J.Zobel, A similarity measure for indefinite rankings, ACM Transactions on Information Systems28(4) (2010), 20:1–20:38.
27.
L.Xu and J.B.Yang, Introduction to multi-criteria decision making and the evidential reasoning approach, in: Working paper series, Paper No. 0106. ISBN:1 86115 111 X, 2001, pp. 1–21.
28.
K.Yang, Y.Cai, D.Huang, J.Li, Z.Zhou and X.Lei, An effective hybrid model for opinion mining and sentiment analysis, in: Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing,BigComp, IEEE, Jeju, South Korea, 2017, pp. 465–466.
29.
H.C.Yu, Z.Y.Lee and S.C.Chang, Using a fuzzy mul-ticriteria decision making approach to evaluate alternative licensing mechanisms, Information & Management42(4) (2005), 517–531.
30.
X.Yu, S.Guo, J.Guo and X.Huang, Rank B2C e-commerce websites in e-alliance based on AHP and fuzzy TOPSIS, Expert Systems with Applications38(4) (2011), 3550–3557.
31.
K.Zhang, R.Narayanan and A.Choudhary, Voice of the customers: Mining online customer reviews for product fea-turebased ranking, in: Proceedings of the 3rdWonference on Online Social Networks, WOSN’10, USENIX Association,Berkeley, CA, USA, 2010, pp. 11–11.
