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Dynamic aspect-based rating system and visualization

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Abstract

With an increasing number of product reviews available online, it has become impractical for potential customers to perceive all the available reviews in order to make an informed decision on their purchase. Product ratings that encapsulate product reviews swiftly and easily have become an alternative for customers. However, since several product ratings only display the overall rating, customers may still find it challenging to make an informed decision due to the lack of information between positive and negative reviews. In addition, existing product ratings are static in nature as they do not cater to customers’ different needs since they often prioritize different aspects of the product or product features. Accordingly, this paper proposes a dynamic aspect-based rating system accompanied by an aspect-based rating visualization to address the aforementioned problems. This rating system also considers the users’ reputations who have given product reviews to give a more holistic view of the users posting reviews. Moreover, our user study shows that our proposed rating visualization can be a competitive alternative in representing a product rating since it has the advantage of being informative and easily customized due to its ability to display rating scores based on users’ preferred aspects. In addition, the proposed visualization also enables customers to make more informed decisions since it displays a balance of both positive and negative reviews.

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References

  • Abbasi Moghaddam, S.: Aspect-based opinion mining in online reviews. PhD thesis, applied sciences: school of computing science (2013)

  • Abdel-Hafez, A., Xu, Y., Tjondronegoro, D.: Product reputation model: an opinion mining based approach. In: Sdad@ ecml/pkdd, pp 16–27 (2012a)

  • Abdel-Hafez, A., Xu, Y., Tjondronegoro, D.: Product reputation model: An opinion mining based approach. In: Sdad@ ecml/pkdd, pp 16–27 (2012b)

  • Abdel-Hafez, A., Xu, Y., Jøsang, A.: A normal-distribution based reputation model. In: International conference on trust, pp. 144–155. Springer, Privacy and Security in Digital Business (2014a)

  • Abdel-Hafez, A., Xu, Y.J.,øsang, A.: A rating aggregation method for generating product reputations. In: Proceedings of the 25th ACM conference on hypertext and social media, pp 291–293 (2014b)

  • Allahbakhsh, M., Ignjatovic, A., Motahari-Nezhad, H.R., Benatallah, B.: Robust evaluation of products and reviewers in social rating systems. World Wide Web 18(1), 73–109 (2015)

    Article  Google Scholar 

  • Bancken, W., Alfarone, D., Davis, J.: Automatically detecting and rating product aspects from textual customer reviews. In: Proceedings of the 1st international workshop on interactions between data mining and natural language processing at ECML/PKDD, vol 1202, pp 1–16 (2014)

  • Basiri, M.E., Naghsh-Nilchi, A.R., Ghasem-Aghaee, N.: Sentiment prediction based on dempster-shafer theory of evidence. Math. Prob. Eng. 2014 (2014)

  • Berger, J., Sorensen, A.T., Rasmussen, S.J.: Positive effects of negative publicity: When negative reviews increase sales. Mark. Sci. 29(5), 815–827 (2010)

    Article  Google Scholar 

  • Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G., Reynar, J.: Building a sentiment summarizer for local service reviews (2008)

  • Brown, J., Morgan, J.: Reputation in online auctions: The market for trust. Calif. Manage. Rev. 49(1), 61–81 (2006)

    Article  Google Scholar 

  • Bucur, C.: Using opinion mining techniques in tourism. Proc. Econ. Fin. 23, 1666–1673 (2015)

    Google Scholar 

  • Cao, Q., Duan, W., Gan, Q.: Exploring determinants of voting for the helpfulness of online user reviews: A text mining approach. Dec. Support Syst. 50(2), 511–521 (2011)

    Article  Google Scholar 

  • Chen, C.W.: Five-star or thumbs-up? the influence of rating system types on users’ perceptions of information quality, cognitive effort, enjoyment and continuance intention. Internet Res. (2017)

  • Chen, L.S., Liu, C.H., Chiu, H.J.: A neural network based approach for sentiment classification in the blogosphere. J. Informet. 5(2), 313–322 (2011)

    Article  Google Scholar 

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

    Google Scholar 

  • Churchill, G.A., Jr., Peter, J.P.: Research design effects on the reliability of rating scales: A meta-analysis. J. Mark. Res. 21(4), 360–375 (1984)

    Article  Google Scholar 

  • Cox, E.P., III.: The optimal number of response alternatives for a scale: A review. J. Mark. Res. 17(4), 407–422 (1980)

    Article  Google Scholar 

  • Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J., Lee, L.: How opinions are received by online communities: a case study on amazon. com helpfulness votes. In: Proceedings of the 18th international conference on World wide web, pp 141–150 (2009)

  • Friedman, H.H., Wilamowsky, Y., Friedman, L.W.: A comparison of balanced and unbalanced rating scales. Mid-Atlant. J. Bus. 19(2), 1–7 (1981)

    Google Scholar 

  • Garcin, F., Faltings, B., Jurca, R.: Aggregating reputation feedback. Proc. First Int. Conf. Reput. Theory Technol. 1, 62–74 (2009)

    Google Scholar 

  • Garcin, F., Faltings, B., Jurca, R.: Aggregating reputation feedback. Proc. First Int. Conf. Reput. Theory Technol. 1, 62–74 (2009)

    Google Scholar 

  • Garland, R.: The mid-point on a rating scale: Is it desirable. Mark. Bull. 2(1), 66–70 (1991)

    Google Scholar 

  • Ghose, A., Ipeirotis, P.G.: Designing ranking systems for consumer reviews: The impact of review subjectivity on product sales and review quality. In: Proceedings of the 16th annual workshop on information technology and systems, pp 303–310 (2006)

  • Hernández-Rubio, M., Cantador, I., Bellogín, A.: A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews. User Model. User Adap. Int. 29(2), 381–441 (2019)

    Article  Google Scholar 

  • Hogenboom, A., Boon, F., Frasincar, F.: A statistical approach to star rating classification of sentiment. In: Management Intelligent Systems, pp. 251–260. Springer, New York (2012)

    Chapter  Google Scholar 

  • Ismail, R., Josang, A.: The beta reputation system. In: BLED 2002 proceedings p 41 (2002)

  • Jacoby, J., Matell, M.S.: Three-point likert scales are good enough (1971)

  • Jamie, B.I.: Users’ perception of product ratings (qualitative quantitative research findings) (2015). https://baymard.com/blog/user-perception-of-product-ratings

  • Jian, Z., Chen, X., Hs, Wang: Sentiment classification using the theory of anns. J. China Univ. Posts Telecommun. 17, 58–62 (2010)

    Google Scholar 

  • Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decis. Support Syst. 43(2), 618–644 (2007)

    Article  Google Scholar 

  • Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decis. Support Syst. 43(2), 618–644 (2007)

    Article  Google Scholar 

  • Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the 2006 Conference on empirical methods in natural language processing, pp 423–430 (2006)

  • Kiritchenko, S., Zhu, X., Cherry, C., Mohammad, S.: Nrc-canada-2014: Detecting aspects and sentiment in customer reviews. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 437–442 (2014)

  • Komorita, S.S.: Attitude content, intensity, and the neutral point on a likert scale. J. Soc. Psychol. 61(2), 327–334 (1963)

    Article  Google Scholar 

  • Korfiatis, N., GarcíA-Bariocanal, E., SáNchez-Alonso, S.: Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs review content. Elect. Comm. Res. Appl. 11(3), 205–217 (2012)

    Article  Google Scholar 

  • Li, G., Liu, F.: A clustering-based approach on sentiment analysis. In: Proceedings of the 2010 IEEE international conference on intelligent systems and knowledge engineering, IEEE, pp 331–337 (2010)

  • Li, R.H., Xu, Y.u. J., Huang, X., Cheng, H.: Robust reputation-based ranking on bipartite rating networks. In: Proceedings of the 2012 SIAM international conference on data mining, SIAM, pp 612–623 (2012)

  • Liao, H., Zeng, A., Xiao, R., Ren, Z.M., Chen, D.B., Zhang, Y.C.: Ranking reputation and quality in online rating systems. PLoS ONE 9, 5 (2014)

    Google Scholar 

  • Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Article  Google Scholar 

  • Maes, A., Ummelen, N., Hoeken, H.: Het ontwerpen van handleidingen. Zoveel hoofden, zoveel handleidingen? In: Handboek Effectief Opleiden, 4.9-6, Delwel, pp 6–01 (1997)

  • Maharani, W., Widyantoro, D.H., Khodra, M.L.: Discovering users perceptions on rating visualizations. In: Proceedings of the 2nd International Conference in HCI and UX Indonesia 2016, pp 31–38 (2016a)

  • Maharani, W., Widyantoro, D.H., Khodra, M.L.: Discovering users perceptions on rating visualizations. In: Proceedings of the 2nd International Conference in HCI and UX Indonesia 2016, pp 31–38 (2016b)

  • Maharani, W., Widyantoro, D.H., Khodra, M.L.: Sae: Syntactic-based aspect and opinion extraction from product reviews. In: Proceedings of the 2015 2nd international conference on advanced informatics: concepts, pp. 1–6. Theory and applications (ICAICTA), IEEE (2015)

  • McAuley, J., Leskovec, J., Jurafsky, D.: Learning attitudes and attributes from multi-aspect reviews. In: Proceedings of the 2012 IEEE 12th International Conference on Data Mining, IEEE, pp 1020–1025 (2012)

  • McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on Recommender systems, pp 165–172 (2013)

  • McGlohon, M., Glance, N., Reiter, Z.: Star quality: Aggregating reviews to rank products and merchants. In: Fourth international AAAI conference on weblogs and social media (2010a)

  • McGlohon, M., Glance, N., Reiter, Z.: Star quality: aggregating reviews to rank products and merchants. In: Fourth international AAAI conference on weblogs and social media (2010b)

  • McNamara, N., Kirakowski, J.: Measuring user-satisfaction with electronic consumer products: the consumer products questionnaire. Int. J. Hum. Comput. Stud. 69(6), 375–386 (2011)

    Article  Google Scholar 

  • Miller, E.: Ranking items with star ratings: an approximate Bayesian approach (2014). http://www.evanmiller.org/ranking-items-with-star-ratings.html

  • Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 188–197 (2019)

  • Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd annual meeting on association for computational linguistics, Association for Computational Linguistics, pp 115–124 (2005)

  • Sabater, J., Sierra, C.: Regret: reputation in gregarious societies. In: Proceedings of the fifth international conference on Autonomous agents, pp 194–195 (2001)

  • Sabater, J., Sierra, C.: Reputation and social network analysis in multi-agent systems. Proc. First Int. Joint Conf. Auton. Agents Multiagent Syst. 1, 475–482 (2002)

    Google Scholar 

  • Said, A., Bellogín, A.: Coherence and inconsistencies in rating behavior: estimating the magic barrier of recommender systems. User Model. User-Adap. Inter. 28(2), 97–125 (2018)

    Article  Google Scholar 

  • Sparling, E.I., Sen, S.: Rating: how difficult is it? In: Proceedings of the fifth ACM conference on Recommender systems, pp 149–156 (2011)

  • Tadano, R., Shimada, K., Endo, T.: Multi-aspects review summarization based on identification of important opinions and their similarity. In: Proceedings of the 24th Pacific Asia conference on language, information and computation, pp 685–692 (2010)

  • Thet, T.T., Na, J.C., Khoo, C.S.: Aspect-based sentiment analysis of movie reviews on discussion boards. J. Inf. Sci. 36(6), 823–848 (2010)

    Article  Google Scholar 

  • Turney, P.D.: 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, Association for Computational Linguistics, pp 417–424 (2002)

  • Uddin, G., Khomh, F.: Automatic summarization of API reviews. In: Proceedings of the 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE, pp 159–170 (2017)

  • Wan, M., Ni, J., Misra, R., McAuley, J.: Addressing marketing bias in product recommendations. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp 618–626 (2020)

  • Ware, C.: Information visualization: perception for design. Morgan Kaufmann (2019)

  • Weijters, B., Cabooter, E., Schillewaert, N.: The effect of rating scale format on response styles: the number of response categories and response category labels. Int. J. Res. Mark. 27(3), 236–247 (2010)

    Article  Google Scholar 

  • Wildt, A.R., Mazis, M.B.: Determinants of scale response: label versus position. J. Mark. Res. 15(2), 261–267 (1978)

    Article  Google Scholar 

  • Wu, Y., Ester, M.: Flame: A probabilistic model combining aspect based opinion mining and collaborative filtering. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp 199–208 (2015)

  • Wu, X., Xie, F., Wu, G., Ding, W.: Personalized news filtering and summarization on the web. In: Proceedings of the 2011 IEEE 23rd international conference on tools with artificial intelligence, IEEE, pp 414–421 (2011)

  • Zacharia, G., Moukas, A., Maes, P.: Collaborative reputation mechanisms for electronic marketplaces. Decis. Support Syst. 29(4), 371–388 (2000)

    Article  Google Scholar 

  • Zhang, K., Cheng, Y., Liao, W.K., Choudhary, A.: Mining millions of reviews: a technique to rank products based on importance of reviews. In: Proceedings of the 13th international conference on electronic commerce, pp 1–8 (2011a)

  • Zhang, Z., Ye, Q., Zhang, Z., Li, Y.: Sentiment classification of internet restaurant reviews written in cantonese. Expert Syst. Appl. 38(6), 7674–7682 (2011b)

    Article  Google Scholar 

  • Zhu, G., Iglesias, C.A.: Computing semantic similarity of concepts in knowledge graphs. IEEE Trans. Knowl. Data Eng. 29(1), 72–85 (2016)

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the reviewers for their useful comments and suggestions. This research is partially supported by Research and Innovation grant from Institute of Technology Bandung. The views contained in this paper are those of the authors and should not be interpreted as representing the official policies of the sponsoring organisations or agencies.

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Maharani, W., Widyantoro, D.H. & Khodra, M.L. Dynamic aspect-based rating system and visualization. User Model User-Adap Inter 32, 1–24 (2022). https://doi.org/10.1007/s11257-021-09308-5

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