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A difference of multimedia consumer’s rating and review through sentiment analysis

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Abstract

With the increase in communication using online and mobile channels, consumers, in purchasing products or services, use online user reviews as an important decision-making tool providing information about other consumers’ experiences. Companies also objectively analyze consumer opinions about their products and services to derive business insights. However, the rating, a quantitative information that serves as the basis for the evaluation and recommendation system of reviews, has the limitation that it does not reflect actual consumers’ opinions or is inappropriate for use in recommendation systems. Sentiment analysis, which identifies the emotions and feelings contained in the online text generated by users, has been proposed as a way to deal with and solve such problems related to existing ratings. The present study aimed to investigate, using the lexicon-based approaches for review sentiment analysis, to whether the rating systems reflect genuine experience, satisfaction, and thoughts of customers. The results of this study demonstrate that reviews do not accurately represent the positive or negative aspects of the reviewed products and services, suggesting the need for future modifications in terms of system and consumer analysis. This study also suggests directions of future system and consumer research.

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References

  1. Allen M (1991) Meta-analysis comparing the persuasiveness of one-sided and two-sided messages. West J Speech Commun 55(4):390–404

    Article  Google Scholar 

  2. Banerjee S, Bhattacharyya S, Bose I (2017) Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decis Support Syst 96:17–26

    Article  Google Scholar 

  3. Boiy E, Moens MF (2009) A machine learning approach to sentiment analysis in multilingual Web texts. Inf Retr 12(5):526–558

    Article  Google Scholar 

  4. Brister JM (1991) Word of mouth communication and their effects in consumer network. Adv Consum Res 18:155–169

    Google Scholar 

  5. Cantallops AS, Salvi F (2014) New consumer behavior: a review of research on eWOM and hotels. Int J Hosp Manag 36:41–51

    Article  Google Scholar 

  6. Devine I, Halpern P (2001) Implicit claims: the role of corporate reputation in value creation. Corp Reput Rev 4(1):42–49

    Article  Google Scholar 

  7. Dhande LL, Patnaik GK (2014) Analyzing sentiment of movie review data using naive Bayes neural classifier. International Journal of Emerging Trends & Technology in Computer Science(IJETTCS) 3(4):313–320

  8. Duan WJ, Gu G, Whinston AB (2008) Do online reviews matter? – an empirical investigation of panel data. Decis Support Syst 45(4):1007–1016

    Article  Google Scholar 

  9. Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data 2(1):5

    Article  Google Scholar 

  10. Ficamos, P., Liu, Y., Chen, W. (2017). A naive Bayes and maximum entropy approach to sentiment analysis: capturing domain-specific data in Weibo. In Big data and smart computing (BigComp), 2017 IEEE International Conference on (pp. 336-339). IEEE.

  11. Gamon M, Aue A, Corston-Oliver S, Ringger E (2005) Pulse: mining customer opinions from free text. In: International symposium on intelligent data analysis. Springer, Berlin, Heidelberg, pp 121–132

    Google Scholar 

  12. Godes D, Mayzlin D (2009) Firm-created word-of-mouth communication: evidence from a field test. Mark Sci 28(4):721–739

    Article  Google Scholar 

  13. Han H, Zhang J, Yang J, Shen Y, Zhang Y (2018) Generate domain-specific sentiment lexicon for review sentiment analysis. Multimed Tools Appl 77(16):21265–21280

    Article  Google Scholar 

  14. Harrison-Walker JL (2001) E-complaining: a content analysis of an internet complaint forum. J Consum Mark 15(5):397–412

    Google Scholar 

  15. Hu, N., Pavlou, P.A., Zhang, J., (2009) Can online reviews reveal a product’s true communication. In: Paper presented at the 7th ACM Conference on Electronic Commerce, 324-330.

  16. Kamel M, Siuky FN, Yazdi HS (2019) Robust sentiment fusion on distribution of news. Multimed Tools Appl:1–26

  17. Litvin SW, Goldsmith RE, Pan B (2008) Electronic word-of-mouth in hospitality and tourism management. Tour Manag 29(3):458–468

    Article  Google Scholar 

  18. Liu B (2012) Sentiment analysis and opinion mining. Synthesis lectures on human language technologies 5(1):1–167

    Article  Google Scholar 

  19. Liu C, Kim N (2015) Methodology for improving the reliability of the rating system for leisure activity information sites : focusing on a movie information site. Journal of Tourism and Leisure Research 27(7):187–200

    Google Scholar 

  20. Luca M, Zervas G (2016) Fake it till you make it: reputation, competition, and yelp review fraud. Manag Sci 62(12):3412–3427

    Article  Google Scholar 

  21. Ma X, Lei X, Zhao G, Qian X (2018) Rating prediction by exploring user’s preference and sentiment. Multimed Tools Appl 77(6):6425–6444

    Article  Google Scholar 

  22. Mauri AG, Minazzi R (2013) Web reviews influence on expectations and purchasing intentions of hotel potential customers. Int J Hosp Manag 34:99–107

    Article  Google Scholar 

  23. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113

    Article  Google Scholar 

  24. Mudambi SM, Schuff D (2010) Research note: what makes a helpful online review? A study of customer reviews on amazon.com. MIS Quart 34(1):185–200

  25. Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender systems handbook. Springer, Boston, pp 1–35

    Chapter  Google Scholar 

  26. Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., and Jin, C.,(2007) Red opal: Product-feature scoring from reviews, Proceedings of the 8th ACM conference on Electronic Commerce (EC-2007), San Diego, California, USA, June 11–15.

  27. Scharl A, Weichselbraun A (2008) An automated approach to investigating the online media coverage of US presidential elections. J Inf Technol Politics 5(1):121–132

    Article  Google Scholar 

  28. Schindler R, Bickart B (2002) Characteristics of online consumer comments valued for hedonic and utilitarian shopping tasks. Adv Consum Res 39:428–429

    Google Scholar 

  29. Vaidya, M. P., Desai, M. P., MeghanaPande, M., ShreyasAmte, M. (2015) Some important features of Mobile device management–an integral part of BYOD. Int J Res 02(10):427–435

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Authors and Affiliations

  1. Business Administration, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, 02504, South Korea

    Sung-Won Lee

  2. Department of MIS, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk, 28644, South Korea

    Guangbo Jiang & Hai-Yan Kong

  3. School of Economics and Management, Taishan University, No. 525 Dongyue Street, Daiyue District, Taian, 271000, Shandong, China

    Chang Liu

Authors
  1. Sung-Won Lee
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  2. Guangbo Jiang
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  3. Hai-Yan Kong
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  4. Chang Liu
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Correspondence to Chang Liu.

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Lee, SW., Jiang, G., Kong, HY. et al. A difference of multimedia consumer’s rating and review through sentiment analysis. Multimed Tools Appl 80, 34625–34642 (2021). https://doi.org/10.1007/s11042-020-08820-x

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  • DOI: https://doi.org/10.1007/s11042-020-08820-x

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