Providing Consistent Opinions from Online Reviews: A Heuristic Stepwise Optimization Approach

Published Online:https://doi.org/10.1287/ijoc.2015.0672

References

  • Andreasen AR (1977) A taxonomy of consumer satisfaction/dissatisfaction measures. J. Consumer Affairs 11(2):11–24.CrossrefGoogle Scholar
  • Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.LinkGoogle Scholar
  • Baker L, Anderson RI (1982) Effects of inconsistent information on text processing: Evidence for comprehension monitoring. Reading Res. Quart. 17(2):281–294.CrossrefGoogle Scholar
  • Bawden D, Robinson L (2009) The dark side of information: Overload, anxiety and other paradoxes and pathologies. J. Inform. Sci. 35(2):180–191.CrossrefGoogle Scholar
  • Biswas D, Zhao G, Lehmann DR (2011) The impact of sequential data on consumer confidence in relative judgments. J. Consumer Res. 37(5):874–887.CrossrefGoogle Scholar
  • Chen Y, Xie J (2008) Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Sci. 54(3):477–491.LinkGoogle Scholar
  • Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: Online book reviews. J. Marketing Res. 43(3):345–354.CrossrefGoogle Scholar
  • Chuang SC, Kao DT, Cheng YH, Chou CA (2012) The effect of incomplete information on the compromise effect. Judgment Decision Making 7(2):196–206.CrossrefGoogle Scholar
  • Cutrell E, Guan Z (2007) What are you looking for?: An eye-tracking study of information usage in Web search. Proc. 25th SIGCHI Conf. Human Factors Comput. Systems, San Jose, CA, 407–416.CrossrefGoogle Scholar
  • Diehl K (2005) When two rights make a wrong: Searching too much in ordered environments. J. Marketing Res. 42(3):313–322.CrossrefGoogle Scholar
  • Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. Proc. Internat. Conf. Web Search Web Data Mining, Palo Alto, CA, 231–240.CrossrefGoogle Scholar
  • Duncan CP, Olshavsky RW (1982) External search: The role of consumer beliefs. J. Marketing Res. 19(1):32–43.CrossrefGoogle Scholar
  • Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. Knowledge Data Engrg. 23(10):1498–1512.CrossrefGoogle Scholar
  • Gill MJ, Swann WB, Silvera DH (1998) On the genesis of confidence. J. Personality Soc. Psych. 75(5):1101–1114.CrossrefGoogle Scholar
  • Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Systems Sci. Cybernetics SSC4(2):100–107.CrossrefGoogle Scholar
  • Hochbaum DS (1997) Approximation Algorithms for NP-Hard Problems (PWS Publishing Company, Boston).Google Scholar
  • Hu M, Liu B (2004) Mining and summarizing customer reviews. Proc. 10th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, Seattle, WA, 168–177.CrossrefGoogle Scholar
  • Jacoby J (1984) Perspectives on information overload. J. Consumer Res. 10(4):432–435.CrossrefGoogle Scholar
  • Jansen BJ, Spink A, Saracevic T (2000) Real life, real users, and real needs: A study and analysis of user queries on the web. Inform. Processing Management 36(2):207–227.CrossrefGoogle Scholar
  • Kamvar M, Kellar M, Patel R, Xu Y (2009) Computers and iPhones and mobile phones, oh my! A logs-based comparison of search users on different devices. Proc. 18th Internat. Conf. World Wide Web, Madrid, Spain 801–810.Google Scholar
  • Kim S-M, Pantel P, Chklovski T, Pennacchiotti M (2006) Automatically assessing review helpfulness. Proc. Conf. Empirical Methods Natural Language Processing, Sydney, Australia, 423–430.CrossrefGoogle Scholar
  • Lappas T, Gunopulos D (2010) Efficient confident search in large review corpora. Balcázar J, Bonchi F, Gionis A, Sebag M, eds. Machine Learning and Knowledge Discovery in Databases, Vol. 6322 (Springer, Berlin/Heidelberg), 195–210.CrossrefGoogle Scholar
  • Lappas T, Crovella M, Terzi E (2012) Selecting a characteristic set of reviews. Proc. 18th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, Beijing, 832–840.CrossrefGoogle Scholar
  • Lee J, Park D-H, Han I (2008) The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Res. Appl. 7(3):341–352.CrossrefGoogle Scholar
  • Li X, Hitt LM, Zhang ZJ (2011) Product reviews and competition in markets for repeat purchase products. J. Management Inform. Systems 27(4):941.CrossrefGoogle Scholar
  • Liu B, Hu M, Cheng J (2005) Opinion observer: Analyzing and comparing opinions on the Web. Proc. 14th Internat. Conf. World Wide Web, Chiba, Japan, 342–351.CrossrefGoogle Scholar
  • Liu H, Yang H, Li W, Wei W, He J, Du X (2008) CRO: A system for online review structurization. Proc. 14th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, Las Vegas, NV, 1085–1088.CrossrefGoogle Scholar
  • Miao Q, Li Q, Zeng D (2010) Fine-grained opinion mining by integrating multiple review sources. J. Amer. Soc. Inform. Sci. Tech. 61(11):2288–2299.CrossrefGoogle Scholar
  • Michael RG, David SJ (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness (WH Freeman & Co., San Francisco).Google Scholar
  • Mudambi SM, Schuff D (2010) What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quart. 34(1):185–200.CrossrefGoogle Scholar
  • Oliver RL (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. J. Marketing Res. 17(4):460–469.CrossrefGoogle Scholar
  • Otterbacher J (2011) Being heard in review communities: Communication tactics and review prominence. J. Comput.-Mediated Comm. 16(3):424–444.CrossrefGoogle Scholar
  • Ou CX, Pavlou PA, Davison RM (2014) Swift guanxi in online marketplaces: The role of computer-mediated communication technologies. MIS Quart. 38(1):209–A24.CrossrefGoogle Scholar
  • Oulasvirta A, Hukkinen JP, Schwartz B (2009) When more is less: The paradox of choice in search engine use. Proc. 32nd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval, Boston, 516–523.CrossrefGoogle Scholar
  • Park D-H, Lee J (2008) eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Res. Appl. 7(4):386–398.CrossrefGoogle Scholar
  • Powell WB (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality, 2nd ed. (John Wiley & Sons, New York).CrossrefGoogle Scholar
  • Rapoza K (2012) Alibaba says sales better than Amazon and eBay…combined. Forbes. Accessed May 10, 2015, http://www.forbes.com/sites/kenrapoza/2012/09/09/alibaba-exec-brags-sales-better-than-amazon-ebay-combined/.Google Scholar
  • Smith SD (2010) Confidence and trading aggressiveness of naïve investors: Effects of information quantity and consistency. Rev. Accounting Stud. 15(2):295–316.CrossrefGoogle Scholar
  • Sniedovich M (2009) Dynamic Programming: Foundations and Principles (CRC Press, Boca Raton, FL).Google Scholar
  • Spreng RA, Page TJ (2001) The impact of confidence in expectations on consumer satisfaction. Psych. Marketing 18(11):1187–1204.CrossrefGoogle Scholar
  • Su Q, Xu X, Guo H, Guo Z, Wu X, Zhang X, Swen B, Su Z (2008) Hidden sentiment association in Chinese web opinion mining. Proc. 17th Internat. Conf. World Wide Web, Beijing, 959–968.CrossrefGoogle Scholar
  • Sun M (2012) How does the variance of product ratings matter? Management Sci. 58(4):696–707.LinkGoogle Scholar
  • Tsaparas P, Ntoulas A, Terzi E (2011) Selecting a comprehensive set of reviews. Proc. 17th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining, San Diego, CA, 168–176.CrossrefGoogle Scholar
  • Tse E (2010) Is it too late to enter China? Harvard Bus. Rev. 88(4):96–101.Google Scholar
  • Vandekerckhove J (2006) General simulated annealing algorithm. MATLAB central file exchange. http://www.mathworks.com/matlabcentral/fileexchange/10548-general-simulated-annealing-algorithm.Google Scholar
  • Wang H, Wei Q, Chen G (2013) From clicking to consideration: A business intelligence approach to estimating consumers’ consideration probabilities. Decision Support Systems 56:397–405.CrossrefGoogle Scholar
  • Weinberg BD, Davis L (2005) Exploring the WOW in online-auction feedback. J. Bus. Res. 58(11):1609–1621.CrossrefGoogle Scholar
  • Xiao B, Benbasat I (2007) E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quart. 31(1):137–209.CrossrefGoogle Scholar
  • Yi J, Niblack W (2005) Sentiment mining in WebFountain. Proc. 21st Internat. Conf. Data Engineering, Tokyo, 1073–1083.Google Scholar
  • Zhang L, Liu B (2011) Identifying noun product features that imply opinions. Proc. 49th Annual Meeting ACM: Human Language Technologies, Portland, OR, 575–580.Google Scholar
  • Zhang J, Chen G, Tang X (2012) Extracting representative information to enhance flexible data queries. IEEE Trans. Neural Networks Learn. Systems 23(6):928–941.CrossrefGoogle Scholar
  • Zhang L, Liu B, Lim SH, O’Brien-Strain E (2010) Extracting and ranking product features in opinion documents. Proc. 23rd Internat. Conf. Comput. Linguistics, Beijing, 1462–1470.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.