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A Study on the Influence of Sentiment and Emotion on Review Helpfulness through Online Reviews of Restaurants

레스토랑의 온라인 리뷰를 통해 감성과 감정이 리뷰 유용성에 미치는 영향에 관한 연구

  • Received : 2021.03.16
  • Accepted : 2021.03.29
  • Published : 2021.03.31

Abstract

Sentiment represents one's own state through the process of change to stimulus, and emotion represents a simple psychological state felt for a certain phenomenon. These two terms tend to be used interchangeably, but their meaning and usage are different. In this study, we try to find out how it affects the helpfulness of reviews by classifying sentiment and emotion through online reviews written by online consumers after purchasing and using various products and services. Recently, online reviews have become a very important factor for businesses and consumers. Helpful reviews play a key role in the decision-making process of potential customers and can be assessed through review helpfulness. The helpfulness of reviews is becoming increasingly important in practice as it is utilized in marketing strategies in business as well as in purchasing decision-making issues of consumers. And academically, the importance of research to find the factors influencing the helpfulness of reviews is growing. In this study, Yelp.com secured reviews on restaurants and conducted a study on how the sentiment and emotion of online reviews affect the helpfulness of reviews. Based on the prior research, a research model including sentiment and emotions for online reviews was built, and text mining analyzes how the sentiment and emotion of online reviews affect the helpfulness of online reviews, and the difference in the effects on emotions It was verified. The results showed that negative sentiment and emotion had a greater effect on review helpfulness, which was consistent with the negative bias theory.

자극에 대한 변화의 과정을 통해 자신의 상태를 나타내는 감성과 어떤 현상에 대해 느끼는 단순한 심리상태를 나타내는 감정은 혼용되어 사용되는 경향이 있으나 그 의미와 쓰임새는 다르다. 본 연구에서는 온라인 소비자들이 다양한 제품과 서비스를 구매하고 사용한 후에 작성한 온라인 리뷰를 통해 감성과 감정을 구분하여 리뷰의 유용성에 어떠한 영향을 미치는지 알아보고자 한다. 최근 온라인 리뷰는 비즈니스 및 소비자에게 매우 중요한 요소로 자리매김하고 있다. 유용한 리뷰는 잠재 고객들의 의사결정 과정에서 핵심적인 역할을 하고 있으며 리뷰 유용성을 통해 평가될 수 있다. 리뷰 유용성은 소비자 개인의 구매 의사결정 문제뿐만 아니라 비즈니스에서 마케팅 전략에 활용됨으로써 실무적 중요성은 점차 커지고 있으며, 학문적으로도 리뷰 유용성의 영향요인을 찾는 연구의 중요성이 커지고 있다. 본 연구에서는 Yelp.com에서 레스토랑에 대한 리뷰를 확보하여 온라인 리뷰의 감성과 감정이 리뷰의 유용성에 어떠한 영향을 미치는지에 대한 연구를 진행하였다. 선행연구를 기반으로 온라인 리뷰에 대한 감성과 감정을 포함한 연구 모형을 구축하였으며, 텍스트 마이닝을 통해 온라인 리뷰의 감성과 감정이 온라인 리뷰의 유용성에 어떠한 영향을 미치는지 분석하고 감정에 대한 영향의 차이가 있는지를 검증하였다. 연구결과에서 부정적인 감성과 감정이 리뷰 유용성에 미치는 영향이 더 크며 이는 부정 편향성 이론과 일치하는 것으로 나타났다. 그리고 각각의 감정이 리뷰 유용성에 미치는 영향이 서로 차이가 있는 것으로 나타났다.

Keywords

References

  1. 김은미 (2020). 온라인 리뷰의 감성트랜드를 활용한 관광 핫스팟 예측모형. 인터넷전자상거래연구, 20(4), 99-111.
  2. 이민철, 윤현식 (2020). 머신러닝을 활용한 가짜리뷰 탐지연구: 사용자 행동 분석을 중심으로. 지식경영연구, 21(3), 177-195. https://doi.org/10.15813/KMR.2020.21.3.010
  3. 이홍주 (2019). 인공지능 속성에 대한 고객 태도 변화: AI 스피커 고객 리뷰 분석을 통함 탐색적 연구. 지식경영연구, 20(2), 25-42. https://doi.org/10.15813/kmr.2019.20.2.002
  4. 정희정, 이현애, 정남호, 구철모 (2018). Which is More Important in Useful Online Review? Heuristic-Systematic Model Perspective. 지식경영연구, 19(4), 1-17.
  5. 조신희, 이문용 (2014). 온라인 제품 리뷰의 유용성 결정 요인 분석을 통한 리뷰 활용 방안 도출. Entrue Journal of Information Technology, 13(1), 29-40.
  6. Ahmad, S. N., and Laroche, M. (2015). How do expressed emotions affect the helpfulness of a product review? Evidence from reviews using latent semantic analysis. International Journal of Electronic Commerce, 20(1), 76-111. https://doi.org/10.1080/10864415.2016.1061471
  7. Baumeister, R. F., Bratslavsky, E., Finkenauer, C., and Vohs, K. D. (2001). Bad is stronger than good. Review of general psychology, 5(4), 323-370. https://doi.org/10.1037//1089-2680.5.4.323
  8. Bigne, J. E., and Andreu, L. (2004). Emotions in segmentation: An empirical study. Annals of Tourism Research, 31(3), 682-696. https://doi.org/10.1016/j.annals.2003.12.018
  9. Chatterjee, S. (2020). Drivers of helpfulness of online hotel reviews: A sentiment and emotion mining approach. International Journal of Hospitality Management, 85, 102356. https://doi.org/10.1016/j.ijhm.2019.102356
  10. Chen, M. J., Farn, C. (2020). Examining the Influence of Emotional Expressions in Online Consumer Reviews on Perceived Helpfulness. Information Processing and Management, 57(6), 102266. https://doi.org/10.1016/j.ipm.2020.102266
  11. Chen, Y., and Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management science, 54(3), 477-491. https://doi.org/10.1287/mnsc.1070.0810
  12. Chen, Y., Deng, S., Kwak, D. H., Elnoshokaty, A., and Wu, J. (2019). A multi-appeal model of persuasion for online petition success: A linguistic cue-based approach. Journal of the Association for Information Systems, 20(2), 105-131 https://doi.org/10.17705/1jais.00530
  13. Clark, M. S., Milberg, S., and Erber, R. (1984). Effects of arousal on judgments of others' emotions. Journal of Personality and Social Psychology, 46(3), 551. https://doi.org/10.1037/0022-3514.46.3.551
  14. Czapinski, J. (1985). Negativity bias in psychology: An analysis of Polish publications. Polish Psychological Bulletin, 16(1), 27-44.
  15. Das, S. R., and Chen, M. Y. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management science, 53(9), 1375-1388. https://doi.org/10.1287/mnsc.1070.0704
  16. Durkaya, B. (2020). Examining the helpfulness of online customer reviews based on review related factors: The moderating effect of product type. Doctoral dissertation, Institute of Science And Technology.
  17. Eslami, S. P., Ghasemaghaei, M., and Hassanein, K. (2018). Which online reviews do consumers find most helpful? A multi-method investigation. Decision Support Systems, 113, 32-42. https://doi.org/10.1016/j.dss.2018.06.012
  18. Estes, Z., and Adelman, J. S. (2008). Automatic vigilance for negative words in lexical decision and naming: Comment on Larsen, Mercer, and Balota (2006). American Psychological Association, 8(4), 441-444.
  19. Fang, B., Ye, Q., Kucukusta, D., and Law, R. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52, 498-506. https://doi.org/10.1016/j.tourman.2015.07.018
  20. Felbermayr, A., and Nanopoulos, A. (2016). The role of emotions for the perceived usefulness in online customer reviews. Journal of Interactive Marketing, 36, 60-76. https://doi.org/10.1016/j.intmar.2016.05.004
  21. Filieri, R., Raguseo, E., and Vitari, C. (2018). When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type. Computers in Human Behavior, 88, 134-142. https://doi.org/10.1016/j.chb.2018.05.042
  22. Fiske, S. T. (1980). Attention and weight in person perception: The impact of negative and extreme behavior. Journal of personality and Social Psychology, 38(6), 889-906. https://doi.org/10.1037/0022-3514.38.6.889
  23. Fredrickson, B., 2009. Positivity. Three Rivers Press, New York.
  24. Gao, B., Li, X., Liu, S., and Fang, D. (2018). How power distance affects online hotel ratings: The positive moderating roles of hotel chain and reviewers' travel experience. Tourism Management, 65, 176-186. https://doi.org/10.1016/j.tourman.2017.10.007
  25. Geetha, M., Singha, P., and Sinha, S. (2017). Relationship between customer sentiment and online customer ratings for hotels: An empirical analysis. Tourism Management, 61, 43-54. https://doi.org/10.1016/j.tourman.2016.12.022
  26. Ghose, A., and Ipeirotis, P. G. (2007, August). Designing novel review ranking systems: predicting the usefulness and impact of reviews. In Proceedings of the ninth international conference on Electronic commerce. ACM, 303-310.
  27. Gretzel, U., and Yoo, K. H. (2008). Use and impact of online travel reviews. Information and communication technologies in tourism 2008, (35-46).
  28. Hanck, C., Arnold, M., Gerber, A., and Schmelzer, M. (2019). Introduction to Econometrics with R. Essen: University of Duisburg-Essen.
  29. Harris, R. B., and Paradice, D. (2007). An investigation of the computer-mediated communication of emotions. Journal of Applied Sciences Research, 3(12), 2081-2090.
  30. Herr, P. M., Kardes, F. R., and Kim, J. (1991). Effects of word-of-mouth and product-attribute information on persuasion: An accessibility-diagnosticity perspective. Journal of consumer research, 17(4), 454-462. https://doi.org/10.1086/208570
  31. Hlee, S., Lee, J., Yang, S. B., and Koo, C. (2016). An empirical examination of online restaurant reviews (Yelp. com): moderating roles of restaurant type and self-image disclosure. In Information and communication technologies in tourism 2016 (pp. 339-353). Springer, Cham.
  32. Hong, H., Xu, D., Wang, G. A., and Fan, W. (2017). Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102, 1-11. https://doi.org/10.1016/j.dss.2017.06.007
  33. Hu, N., Bose, I., Koh, N. S., and Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision support systems, 52(3), 674-684. https://doi.org/10.1016/j.dss.2011.11.002
  34. Huang, S., Shen, D., Feng, W., Baudin, C., and Zhang, Y. (2010). Promote product reviews of high quality on e-commerce sites. Pacific Asia Journal of the Association for Information Systems, 2(3), 51-71.
  35. Hwang, S. Y., Lai, C. Y., Chang, S., and Jiang, J. J. (2014). The identification of noteworthy hotel reviews for hotel management. Pacific Asia Journal of the Association for Information Systems, 6(4), 1-17.
  36. Ito, T. A., Larsen, J. T., Smith, N. K., and Cacioppo, J. T. (1998). Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations. Journal of personality and social psychology, 75(4), 887. https://doi.org/10.1037/0022-3514.75.4.887
  37. Kanouse, D. E., and Hanson Jr, L. R. (1987). Negativity in evaluations. In Preparation of this paper grew out of a workshop on attribution theory held at University of California, Los Angeles, Aug 1969. Lawrence Erlbaum Associates, Inc.
  38. Karimi, S., and Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39-48. https://doi.org/10.1016/j.dss.2017.02.001
  39. Kempf, D. S., and Smith, R. E. (1998). Consumer processing of product trial and the influence of prior advertising: A structural modeling approach. Journal of Marketing Research, 35(3), 325-338. https://doi.org/10.2307/3152031
  40. Kennedy, P. 2008. A Guide to Econometrics (6th ed.), Oxford, England: Blackwell Publishers.
  41. Ku, Y. C., Wei, C. P., and Hsiao, H. W. (2012). To whom should I listen? Finding reputable reviewers in opinion-sharing communities. Decision Support Systems, 53(3), 534-542. https://doi.org/10.1016/j.dss.2012.03.003
  42. Lee, S., and Choeh, J. Y. (2018). The interactive impact of online word-of-mouth and review helpfulness on box office revenue. Management Decision. 56(4), 849-866. https://doi.org/10.1108/MD-06-2017-0561
  43. Lerner, J. S., and Tiedens, L. Z. (2006). Portrait of the angry decision maker: How appraisal tendencies shape anger's influence on cognition. Journal of behavioral decision making, 19(2), 115-137. https://doi.org/10.1002/bdm.515
  44. Lerner, J. S., and Keltner, D. (2000). Beyond Valence: Toward a Model of Emotion-Specific Influences on Judgement and Choice. Cognition and Emotion, 14(4), 473-493. https://doi.org/10.1080/026999300402763
  45. Lerner, J. S., and Keltner, D. (2001). Fear, Anger, and Risk. Journal of Personality and Social Psychology, 81(1), 146-159. https://doi.org/10.1037/0022-3514.81.1.146
  46. Levenson, R. W., Ekman, P., and Friesen, W. V. (1990). Voluntary facial action generates emotion-specific autonomic nervous system activity. Psychophysiology, 27(4), 363-384. https://doi.org/10.1111/j.1469-8986.1990.tb02330.x
  47. Li, S. T., Pham, T. T., and Chuang, H. C. (2019). Do reviewers' words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles. Information and Management, 56(1), 28-38. https://doi.org/10.1016/j.im.2018.06.002
  48. Li, Y. (2019). Consumers' perceived usefulness of online reviews: Effects of emotional certainty and product involvement. Social Behavior and Personality: an international journal, 47(9), 1-16.
  49. Li, Y. (2019). Effects of Emotional Certainty on the Perceived Usefulness of Online Reviews, Journal of Mathematics and Informatics, 17, 97-106. https://doi.org/10.22457/jmi.146av17a9
  50. Li, H., Liu, H., and Zhang, Z. (2020). Online persuasion of review emotional intensity: A text mining analysis of restaurant reviews. International Journal of Hospitality Management, 89, 102558. https://doi.org/10.1016/j.ijhm.2020.102558
  51. Lim, N. (2016). Cultural differences in emotion: differences in emotional arousal level between the East and the West. Integrative medicine research, 5(2), 105-109. https://doi.org/10.1016/j.imr.2016.03.004
  52. Liu, Z., and Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140-151. https://doi.org/10.1016/j.tourman.2014.09.020
  53. Lu, Y., Kong, X., Quan, X., Liu, W., and Xu, Y. (2010, July). Exploring the sentiment strength of user reviews. In International Conference on Web-Age Information Management, 471-482.
  54. Luca, M. (2016). Reviews, Reputation, and Revenue: The Case of Yelp.com. Harvard Business School Working Paper, 12-016, (pp.1-39).
  55. Malik, M. S. I., and Hussain, A. (2017). Helpfulness of product reviews as a function of discrete positive and negative emotions. Computers in Human Behavior, 73, 290-302. https://doi.org/10.1016/j.chb.2017.03.053
  56. Martin, L., and Pu, P. (2014, June). Prediction of helpful reviews using emotions extraction. In Twenty-Eighth AAAI conference on artificial intelligence, 1551-1557.
  57. Mouthami, K., Devi, K. N., and Bhaskaran, V. M. (2013, February). Sentiment analysis and classification based on textual reviews. In 2013 international conference on Information communication and embedded systems (ICICES), 271-276.
  58. Mudambi, S. M., and Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon. Com. MIS quarterly, 34(1), 185-200. https://doi.org/10.2307/20721420
  59. Nabi, R. (2002). Anger, fear, uncertainty, and attitudes: A test of the cognitive-functional model. Communication Monographs, 69(3), 204-216. https://doi.org/10.1080/03637750216541
  60. Nabi, R. L. (1999). A cognitive-functional model for the effects of discrete negative emotions on information processing, attitude change, and recall. Communication theory, 9(3), 292-320. https://doi.org/10.1111/j.1468-2885.1999.tb00172.x
  61. Nakayama, M., and Wan, Y. (2019). The cultural impact on social commerce: A sentiment analysis on Yelp ethnic restaurant reviews. Information and Management, 56(2), 271-279. https://doi.org/10.1016/j.im.2018.09.004
  62. Nelson, P. (1970). Information and consumer behavior. Journal of political economy, 78(2), 311-329. https://doi.org/10.1086/259630
  63. Nyer, P. U. (1997). A study of the relationships between cognitive appraisals and consumption emotions. Journal of the Academy of Marketing Science, 25(4), 296-304. https://doi.org/10.1177/0092070397254002
  64. Park, D. H., Lee, J., and Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International journal of electronic commerce, 11(4), 125-148. https://doi.org/10.2753/JEC1086-4415110405
  65. Park, S., and Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67-83. https://doi.org/10.1016/j.annals.2014.10.007
  66. Petty, R. E., and Brinol, P. (2015). Emotion and persuasion: Cognitive and meta-cognitive processes impact attitudes. Cognition and Emotion, 29(1), 1-26. https://doi.org/10.1080/02699931.2014.967183
  67. Plutchik, R. (1994). The psychology and biology of emotion. New York, NY, US: HarperCollins College Publishers.
  68. Rana, T. A., and Cheah, Y.-N. (2016). Aspect extraction in sentiment analysis: comparative analysis and survey. Artificial Intelligence Review, 46(4), 459-483. https://doi.org/10.1007/s10462-016-9472-z
  69. Ren, G., and Hong, T. (2019). Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews. Information Processing and Management, 56(4), 1425-1438. https://doi.org/10.1016/j.ipm.2018.04.003
  70. Rocklage, M. D., and Fazio, R. H. (2020). The enhancing versus backfiring effects of positive emotion in consumer reviews. Journal of Marketing Research, 57(2), 332-352. https://doi.org/10.1177/0022243719892594
  71. Roseman, I. J. (1984). Cognitive determinants of emotion: A structural theory. Review of personality and social psychology, 5, 11-36.
  72. Roseman, I. J., Wiest, C., and Swartz, T. S. (1994). Phenomenology, behaviors, and goals differentiate discrete emotions. Journal of personality and social psychology, 67(2), 206-221. https://doi.org/10.1037/0022-3514.67.2.206
  73. Rozin, P., and Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and social psychology review, 5(4), 296-320. https://doi.org/10.1207/S15327957PSPR0504_2
  74. Russell, J. A., and Barrett, L. F. (1999). Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. Journal of personality and social psychology, 76(5), 805. https://doi.org/10.1037/0022-3514.76.5.805
  75. Salehan, M., and Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40. https://doi.org/10.1016/j.dss.2015.10.006
  76. Sanbonmatsu, D. M., and Kardes, F. R. (1988). The effects of physiological arousal on information processing and persuasion. Journal of Consumer research, 15(3), 379-385. https://doi.org/10.1086/209175
  77. Septianto, F., and Chiew, T. M. (2018). The effects of different, discrete positive emotions on electronic word-of-mouth. Journal of Retailing and Consumer Services, 44, 1-10. https://doi.org/10.1016/j.jretconser.2018.05.006
  78. Reisenzein, R. (1994). Pleasure-arousal theory and the intensity of emotions. Journal of personality and social psychology, 67(3), 525. https://doi.org/10.1037/0022-3514.67.3.525
  79. Shaver, P., Schwartz, J., Kirson, D., and O'connor, C. (1987). Emotion knowledge: further exploration of a prototype approach. Journal of personality and social psychology, 52(6), 1061. https://doi.org/10.1037/0022-3514.52.6.1061
  80. Siering, M., Muntermann, J., and Rajagopalan, B. (2018). Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108, 1-12. https://doi.org/10.1016/j.dss.2018.01.004
  81. Smith, C. A., and Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. Journal of personality and social psychology, 48(4), 813-838. https://doi.org/10.1037/0022-3514.48.4.813
  82. Smith, C. A., and Lazarus, R. S. (1990). Emotion and adaptation. Handbook of personality: Theory and research, 609-637.
  83. Solomon, R. C. (1993). The philosophy of emotions. Handbook of emotions, 2, 5-13.
  84. Sparks, B. A., and Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism management, 32(6), 1310-1323. https://doi.org/10.1016/j.tourman.2010.12.011
  85. Tiedens, L. Z. (2001). The effect of anger on the hostile inferences of aggressive and nonaggressive people: Specific emotions, cognitive processing, and chronic accessibility. Motivation and Emotion, 25(3), 233-251. https://doi.org/10.1023/A:1012224507488
  86. Tiedens, L. Z., and Linton, S. (2001). Judgment under emotional certainty and uncertainty: the effects of specific emotions on information processing. Journal of personality and social psychology, 81(6), 973. https://doi.org/10.1037/0022-3514.81.6.973
  87. Turney, P. D. (2002, July). Thumbs up or thumbs down? : semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics, 417-424.
  88. Ullah, R., Zeb, A., and Kim, W. (2015). The impact of emotions on the helpfulness of movie reviews. Journal of applied research and technology, 13(3), 359-363. https://doi.org/10.1016/j.jart.2015.02.001
  89. Vanhamme, J., and Snelders, D. (2001). The role of surprise in satisfaction judgments. Journal of Consumer Satisfaction Dissatisfaction and Complaining Behavior, 14, 27-45.
  90. Vermeulen, I. E., and Seegers, D. (2009). Tried and tested: The impact of online hotel reviews on consumer consideration. Tourism management, 30(1), 123-127. https://doi.org/10.1016/j.tourman.2008.04.008
  91. Wang, X., Tang, L. R., and Kim, E. (2019). More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness?. International Journal of Hospitality Management, 77, 438-447. https://doi.org/10.1016/j.ijhm.2018.08.007
  92. Westbrook, R. A., and Oliver, R. L. (1991). The dimensionality of consumption emotion patterns and consumer satisfaction. Journal of consumer research, 18(1), 84-91. https://doi.org/10.1086/209243
  93. Xiang, Z., Du, Q., Ma, Y., and Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65. https://doi.org/10.1016/j.tourman.2016.10.001
  94. Xia, L., and Bechwati, N. N. (2008). Word of mouse: the role of cognitive personalization in online consumer reviews. Journal of interactive Advertising, 9(1), 3-13. https://doi.org/10.1080/15252019.2008.10722143
  95. Yin, D., Bond, S., and Zhang, H. (2014). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2), 539-560. https://doi.org/10.25300/MISQ/2014/38.2.10
  96. Yin, G., Wei, L., Xu, W., and Chen, M. (2014). Exploring Heuristic cues for Consumer Perceptions of Online Reviews Helpfulness: the Case of Yelp. Com. PACIS, 52.
  97. Yu, Y., Duan, W., and Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919-926. https://doi.org/10.1016/j.dss.2012.12.028
  98. Zablocki, A., Makri, K., and Houston, M. J. (2019). Emotions within online reviews and their influence on product attitudes in Austria, USA and Thailand. Journal of Interactive Marketing, 46, 20-39. https://doi.org/10.1016/j.intmar.2019.01.001
  99. Zhao, Y., Xu, X., and Wang, M. (2019). Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews. International Journal of Hospitality Management, 76, 111-121. https://doi.org/10.1016/j.ijhm.2018.03.017
  100. Zhou, S., and Guo, B. (2015, September). The interactive effect of review rating and text sentiment on review helpfulness. In International Conference on Electronic Commerce and Web Technologies (pp. 100-111). Springer, Cham.
  101. Zhu, L., Yin, G., and He, W. (2014). Is this opinion leader's review useful? Peripheral cues for online review helpfulness. Journal of Electronic Commerce Research, 15(4), 267.