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Exploring the influential reviewer, review and product determinants for review helpfulness

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Abstract

Helpfulness of online reviews is a multi-faceted concept. The reviews are usually ranked on the basis of perceived helpful votes and aid in making purchase decisions for online customers. This study extends the prior work done for review helpfulness by considering not only the influential characteristics of reviews but also incorporates influential indicators of reviewer and product category. Influential factor based new features (product, reviewer and review) are proposed to predict the helpfulness of online reviews by using five ML methods. The experimental analysis on a real-life review dataset shows that the hybrid set of proposed features deliver the best predictive performance. In addition, the reviewer and the review category features introduced in this research exhibit better predictive performance as a standalone model. Findings show that reviews which have large number of comments, large values of sentiment and polarity scores receive more helpful votes. The reviewer activity length and recency are statistically significant predictors for helpfulness prediction. In addition, number of question answered, ratio of positive reviews and average rating per review are also significant variables of product type. The findings of this study highlight the number of implications for research and provide new insights to retailers for efficient ranking and organization of consumer reviews for online users.

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References

  • Agnihotri A, Bhattacharya S (2016) Online review helpfulness: role of qualitative factors. Psychol Mark 33(11):1006–1017

    Article  Google Scholar 

  • Anderson M, Magruder J (2012) Learning from the crowd: regression discontinuity estimates of the effects of an online review database. Econ J 122(563):957–989

    Article  Google Scholar 

  • Banerjee S, Chua AY et al (2015) Let’s vote to classify authentic and manipulative online reviews: the role of comprehensibility, informativeness and writing style. In: Science and information conference (SAI), 2015, IEEE

  • Bertola F, Patti V (2016) Ontology-based affective models to organize artworks in the social semantic web. Inf Process Manag 52(1):139–162

    Article  Google Scholar 

  • Cao Q, Duan W et al (2011) Exploring determinants of voting for the “helpfulness” of online user reviews: a text mining approach. Decis Support Syst 50(2):511–521

    Article  Google Scholar 

  • Casas P, Seufert M et al (2018) Enhancing machine learning based QoE prediction by ensemble models. In: 2018 IEEE 38th international conference on distributed computing systems (ICDCS), IEEE

  • Chen M-Y (2016) Can two-sided messages increase the helpfulness of online reviews? Online Inf Rev 40(3):316–332

    Article  Google Scholar 

  • Chen H-N, Huang C-Y (2013) An investigation into online reviewers’ behavior. Eur J Mark 47(10):1758–1773

    Article  Google Scholar 

  • Chen Y, Xie J (2008) Online consumer review: word-of-mouth as a new element of marketing communication mix. Manag Sci 54(3):477–491

    Article  Google Scholar 

  • Chen Y, Chai Y et al (2015) Analysis of review helpfulness based on consumer perspective. Tsinghua Sci Technol 20(3):293–305

    Article  Google Scholar 

  • Chua AY, Banerjee S (2015) Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth. J Assoc Inf Sci Technol 66(2):354–362

    Article  Google Scholar 

  • Chua AY, Banerjee S (2016) Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Comput Hum Behav 54:547–554

    Article  Google Scholar 

  • Crowley AE, Hoyer WD (1994) An integrative framework for understanding two-sided persuasion. J Consum Res 20(4):561–574

    Article  Google Scholar 

  • Duan W, Gu B et al (2008) The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry. J Retail 84(2):233–242

    Article  Google Scholar 

  • Forman C, Ghose A et al (2008) Examining the relationship between reviews and sales: the role of reviewer identity disclosure in electronic markets. Inf Syst Res 19(3):291–313

    Article  Google Scholar 

  • Gao B, Hu N et al (2016) Follow the herd or be myself? An analysis of consistency in behavior of reviewers and helpfulness of their reviews. Decis Support Syst

  • Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498–1512

    Article  Google Scholar 

  • Hammond JS, Keeney RL et al (1998) The hidden traps in decision making. Harvard Bus Rev 76(5):47–58

    Google Scholar 

  • Healey SP, Cohen WB et al (2018) Mapping forest change using stacked generalization: an ensemble approach. Remote Sens Environ 204:717–728

    Article  Google Scholar 

  • Ho TK, Hull JJ et al (1994) Decision combination in multiple classifier systems. IEEE Trans Pattern Anal Mach Intell 16(1):66–75

    Article  Google Scholar 

  • Hong Y, Lu J et al (2012) What reviews are satisfactory: novel features for automatic helpfulness voting. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, ACM

  • Hu Y-H, Chen K (2016) Predicting hotel review helpfulness: the impact of review visibility, and interaction between hotel stars and review ratings. Int J Inf Manag 36(6):929–944

    Article  Google Scholar 

  • Hu N, Zhang J et al (2009) Overcoming the J-shaped distribution of product reviews. Commun ACM 52(10):144–147

    Article  Google Scholar 

  • Huang AH, Yen DC (2013) Predicting the helpfulness of online reviews—a replication. Int J Hum Comput Interact 29(2):129–138

    Article  MathSciNet  Google Scholar 

  • Huang AH, Chen K et al (2015) A study of factors that contribute to online review helpfulness. Comput Hum Behav 48:17–27

    Article  Google Scholar 

  • Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz 53(1):59–68

    Article  Google Scholar 

  • Karimi S, Wang F (2016) Factors affecting online review helpfulness: review and reviewer components. Rediscovering the essentiality of marketing. Springer, Berlin, p 273

    Google Scholar 

  • Kim S-M, Pantel P et al (2006) Automatically assessing review helpfulness. In: Proceedings of the 2006 conference on empirical methods in natural language processing, association for computational linguistics

  • Korfiatis N, García-Bariocanal E et al (2012) Evaluating content quality and helpfulness of online product reviews: the interplay of review helpfulness vs. review content. Electron Commer Res Appl 11(3):205–217

    Article  Google Scholar 

  • Krishnamoorthy S (2015) Linguistic features for review helpfulness prediction. Expert Syst Appl 42(7):3751–3759

    Article  Google Scholar 

  • Lee S, Choeh JY (2014) Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst Appl 41(6):3041–3046

    Article  Google Scholar 

  • Lee S, Choeh JY (2016) The determinants of helpfulness of online reviews. Behav Inf Technol 1–11

  • Liu J, Cao Y et al (2007) Low-Quality product review detection in opinion summarization. EMNLP-CoNLL

  • Liu Y, Huang X et al (2008) Modeling and predicting the helpfulness of online reviews. In: 2008 eighth IEEE international conference on data mining, IEEE

  • Liu Z, Jansen BJ (2017) Identifying and predicting the desire to help in social question and answering. Inf Process Manag 53(2):490–504

    Article  Google Scholar 

  • Liu Z, Park S (2015) What makes a useful online review? Implication for travel product websites. Tour Manag 47:140–151

    Article  Google Scholar 

  • Malik M, Hussain A (2017) Helpfulness of product reviews as a function of discrete positive and negative emotions. Comput Hum Behav 73:290–302

    Article  Google Scholar 

  • Malik M, Hussain A (2018) An analysis of review content and reviewer variables that contribute to review helpfulness. Inf Process Manag 54(1):88–104

    Article  Google Scholar 

  • Mudambi SM, Schuff D (2010) What makes a helpful review? A study of customer reviews on Amazon.com. MIS Q 34(1):185–200

    Article  Google Scholar 

  • Ngo-Ye TL, Sinha AP (2014) The influence of reviewer engagement characteristics on online review helpfulness: a text regression model. Decis Support Syst 61:47–58

    Article  Google Scholar 

  • Ngo-Ye TL, Sinha AP et al (2017) Predicting the helpfulness of online reviews using a scripts-enriched text regression model. Expert Syst Appl 71:98–110

    Article  Google Scholar 

  • Otterbacher, J. (2009). ‘Helpfulness’ in online communities: a measure of message quality. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM

  • Pan Y, Zhang JQ (2011) Born unequal: a study of the helpfulness of user-generated product reviews. J Retail 87(4):598–612

    Article  Google Scholar 

  • Pang B, Lee L et al (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

  • Qazi A, Syed KBS et al (2016) A concept-level approach to the analysis of online review helpfulness. Comput Hum Behav 58:75–81

    Article  Google Scholar 

  • Quaschning S, Pandelaere M et al (2015) When consistency matters: the effect of valence consistency on review helpfulness. J Comput Med Commun 20(2):136–152

    Article  Google Scholar 

  • Singh JP, Irani S et al (2017) Predicting the “helpfulness” of online consumer reviews. J Bus Res 70:346–355

    Article  Google Scholar 

  • Stieglitz S, Dang-Xuan L (2013) Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior. J Manag Inf Syst 29(4):217–248

    Article  Google Scholar 

  • Thelwall M, Buckley K et al (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61(12):2544–2558

    Article  Google Scholar 

  • Tsur O, Rappoport A (2009) RevRank: a fully unsupervised algorithm for selecting the most helpful book reviews. ICWSM

  • Ullah R, Zeb A et al (2015) The impact of emotions on the helpfulness of movie reviews. J Appl Res Technol 13(3):359–363

    Article  Google Scholar 

  • Ullah R, Amblee N et al (2016) From valence to emotions: exploring the distribution of emotions in online product reviews. Decis Support Syst 81:41–53

    Article  Google Scholar 

  • Willemsen LM, Neijens PC et al (2011) “Highly Recommended!” The content characteristics and perceived usefulness of online consumer reviews. J Comput Med Commun 17(1):19–38

    Article  Google Scholar 

  • Wu J (2017) Review popularity and review helpfulness: a model for user review effectiveness. Decis Support Syst 97:92–103

    Article  Google Scholar 

  • Xiang C, Sun Y (2016) Research on the evaluation index system of online reviews helpfulness. In: 2016 13th international conference on service systems and service management (ICSSSM), IEEE

  • Yang S-B, Shin S-H et al (2016) Exploring the comparative importance of online hotel reviews’ heuristic attributes in review helpfulness: a conjoint analysis approach. J Travel Tour Mark 1–23

  • Zhang Z (2008) Weighing stars: aggregating online product reviews for intelligent e-commerce applications. IEEE Intell Syst 23(5):42–49

    Article  Google Scholar 

  • Zhang Z, Varadarajan B (2006) Utility scoring of product reviews. In: Proceedings of the 15th ACM international conference on Information and knowledge management, ACM

  • Zhang Z, Wei Q et al (2014) Estimating online review helpfulness with probabilistic distribution and confidence. Foundations and applications of intelligent systems. Springer, Berlin, pp 411–420

    Google Scholar 

  • Zhou S, Guo B (2017) The order effect on online review helpfulness: a social influence perspective. Decis Support Syst 93:77–87

    Article  Google Scholar 

Download references

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Science, Comsat University Islamabad, Attock Campus, Attock, Pakistan

    M. S. I. Malik

  2. Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan

    Ayyaz Hussain

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  1. M. S. I. Malik
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  2. Ayyaz Hussain
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Correspondence to Ayyaz Hussain.

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Malik, M.S.I., Hussain, A. Exploring the influential reviewer, review and product determinants for review helpfulness. Artif Intell Rev 53, 407–427 (2020). https://doi.org/10.1007/s10462-018-9662-y

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