Top

Published in:

2020 | OriginalPaper | Chapter

30. Analyse der Nützlichkeit von Amazon-Produktbewertungen mittels Text Mining

Authors : Florian Bosten, Manuel Di Stefano, Maren Hartmann, Sebastian Sauer, Rüdiger Buchkremer

Published in: Künstliche Intelligenz in Wirtschaft & Gesellschaft

Publisher: Springer Fachmedien Wiesbaden

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Zusammenfassung

Produktrezensionen haben inzwischen einen höheren Stellenwert bekommen als andere Marketingwerkzeuge wie Fragen- und Antwortseiten oder Herstellerforen. In diesem Beitrag wird gezeigt, wie stilistische und semantische Texteigenschaften die vom Leser empfundene Nützlichkeit und sein Abstimmungsverhalten im Hinblick auf dieses Empfinden beeinflussen. Zusätzlich dazu werden in einem separaten Schritt Meta-Informationen, in Form der von Amazon verliehenen Badges, untersucht. Dafür sind nahezu zwei Millionen Amazon-Kundenbewertungen erhoben worden. Dabei kann gezeigt werden, dass sowohl stilistische als auch semantische Eigenschaften und Meta-Informationen zum einen die empfundene Nützlichkeit als auch das Abstimmungsverhalten beeinflussen. Die Untersuchung von unterschiedlichen Produktkategorien zeigt, dass die Beobachtungen grundsätzlicher Natur und nicht auf eine einzelne Sparte oder ein einzelnes Produkt begrenzt sind. Die Ergebnisse zeigen auf, welche Kriterien ein Autor einer Bewertung berücksichtigen muss, um mit seiner Rezension an Reichweite zu gelangen.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Vergleichende Analyse der Word-Embedding-Verfahren Word2Vec und GloVe am Beispiel von Kundenbewertungen eines Online-Versandhändlers

next chapter Analyse von Nützlichkeits- und Sterne-Online-Bewertungen mittels Machine Learning am Beispiel von Amazon

Zur einfacheren Lesbarkeit wird das generische Maskulinum in diesem Beitrag verwendet. An jeder Stelle sind alle Geschlechter in gleichem Maße angesprochen.

Amazon. (2019). About Amazon verified purchase reviews [WWW Document]. https://www.amazon.com/gp/help/customer/display.html?nodeId=201145140/. Zugegriffen: 31. Aug. 2019.

Amazon. (2019b). Community-Richtlinien [WWW Document]. https://www.amazon.de/gp/help/customer/display.html?nodeId=201929730/. Zugegriffen: 31. Aug. 2019.

Amazon. (2019c). What is Amazon Vine? [WWW Document]. https://www.amazon.com/gp/vine/help/. Zugegriffen: 31. Aug. 2019.

Amazon. (2019d). About badges [WWW Document]. https://www.amazon.com/gp/help/customer/display.html?ie=UTF8&nodeId=14279681. Zugegriffen: 31. Aug. 2019.

Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers’ objectives and review cues. International Journal of Electronic Commerce, 17, 99–126. https://doi.org/10.2753/jec1086-4415170204.CrossRef

BazaarVoice. (2015). The conversation index [WWW Document]. http://media2.bazaarvoice.com/documents/Bazaarvoice-Conversation-Index-9-DIGITAL.pdf. Zugegriffen: 11. Aug. 2019.

Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economics Behaviour, 52, 336–372. https://doi.org/10.1016/j.geb.2004.06.010.CrossRef

Borgida, E., & Nisbett, R. E. (1977). The differential impact of abstract vs. concrete information on decisions. Journal of Applied Social Psychology, 7(3), 258–271.

Chua, A. Y. K., & Banerjee, S. (2014). Developing a theory of diagnosticity for online reviews. In S. I. Ao, O. Castillo, C. Douglas, D. D. Feng, & J.-A. Lee (Hrsg.), Lecture notes in engineering and computer science (S. 477–482). Hong Kong: Newswood Limited.

Chua, A. Y. K., & Banerjee, S. (2016). Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Computers in Human Behavior, 54, 547–554. https://doi.org/10.1016/j.chb.2015.08.057.CrossRef

Cialidini, R. B. (2001). Science and practice (4. Aufl.). Boston: Needham Heights.

Coleman, M., & Liau, T. L. (1975). A computer readability formula designed for machine scoring. Journal of Applied Psychology, 60, 283–284. https://doi.org/10.1037/h0076540.CrossRef

Connors, L., Mudambi, S. M., & Schuff, D. (2011). Is it the review or the reviewer? A multi-method approach to determine the antecedents of online review helpfulness. In R. H. Sprague (Hrsg.), Proceedings of the annual Hawaii international conference on system sciences (S. 1–10). https://doi.org/10.1109/hicss.2011.260.

Dhanasobhon, S., Chen, P., Smith, M., & Smith, M. D. (2007). An analysis of the differential impact of reviews and reviewers at Amazon.com. Information Systems.

DuBay, W. (2004). The principles of readability, Online Submission. Costa Mesa: ERIC.

Esuli, A. (2015). Amazon reviews downloader and parser [WWW Document]. https://github.com/aesuli/Amazon-downloader. Zugegriffen: 11. Aug. 2019.

Flesch, R. (1949). The art of readable writing, stanford law review. New York: Harper. https://doi.org/10.2307/1225957.CrossRef

Freedman, L. (2011). The 2011 social shopping study table of contents [WWW Document]. https://de.slideshare.net/retelur/the-2011-social-shopping-study-the-etail-group-powers-reviews-sep11. Zugegriffen: 11. Aug. 2019.

Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23, 1498–1512. https://doi.org/10.1109/TKDE.2010.188.CrossRef

Grice, H. P. (1975). Logic and conversation. Syntax and Semantics 3(speech acts 3), 41–58. https://doi.org/10.1057/9780230005853_5.

Guéguen, N., & Jacob, C. (2002). Solicitation by e-mail and solicitor’s status: A field study of social influence on the web. Cyberpsychology Behavior, 5, 377–383. https://doi.org/10.1089/109493102760275626.CrossRef

Gunning, R. (1952). The technique of clear writing, 1952. New York: McGraw-Hill.

Hamilton, M. A., & Nowak, K. L. (2005). Information systems concepts across two decades: An empirical analysis of trends in theory, methods, process, and research domains. Journal of Communication 55(3), 529–553.

Hammond, J. S., Keeney, R. L., & Raiffa, H. (1998). The hidden traps in decision making. Harvard Business Review, 76, 47–54.

Han, H. J. „Spring“, Mankad, S., Gavirneni, N., & Verma, R. (2016). What guests really think of your hotel: Text analytics of online customer reviews. Cornell Hospitality Report, 16, 3–17.

Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18, 38–52. https://doi.org/10.1002/dir.10073.CrossRef

Hu, N., Pavlou, P.A., & Zhang, J. (2006). Can online reviews reveal a product’s true quality? Empirical findings analytical modeling of online word-of-mouth communication. In Proceedings of ACM conference electronic commerce (S. 324–330).

Hu, N., Zhang, J., & Pavlou, P. A. (2009). Overcoming the J-shaped distribution of product reviews. Communications of the ACM, 52, 144. https://doi.org/10.1145/1562764.1562800.CrossRef

Jessmer, S. L., & Anderson, D. (2001). The effect of politeness and grammar on user perceptions of electronic mail. North American Journal of Pscyhology, 3, 331–346.

Johnson, E. J., & Payne, J. W. (1985). Effort and accuracy in choice. Management Science, 31, 395–414. https://doi.org/10.1287/mnsc.31.4.395.CrossRef

Jones, Q., Ravid, G., & Rafaeli, S. (2004). Information overload and the message dynamics of online interaction spaces: A theoretical model and empirical exploration. Information Systems Research, 15, 194–211. https://doi.org/10.1287/isre.1040.0023.CrossRef

Kate, R. J., Luo, X., Patwardhan, S., Franz, M., Florian, R., Mooney, R. J., Roukos, S., & Welty, C. (2010). Learning to predict readability using diverse linguistic features. In C.-R. Huang & D. Jurafsky (Hrsg.), Coling 2010 – 23rd international conference on computational linguistics, proceedings of the conference (S. 546–554).

Kokkodis, M. (2012). Learning from positive and unlabeled Amazon reviews: Towards identifying trustworthy reviewers. In WWW’12 – Proceedings of 21st annual conference World Wide Web Companion (S. 545–546). https://doi.org/10.1145/2187980.2188119.

Korfiatis, N. (2011). Evaluating content quality and usefulness of online product reviews. SSRN Electronic Journal, 11, 205–217. https://doi.org/10.2139/ssrn.1156321.CrossRef

Korfiatis, N., & Rodr, D. (2000). The impact of readability on the usefulness of online product reviews: A case study on an. Emerging Technologies and Information Systems for the Knowledge Society, 5288, 423–432.CrossRef

Korfiatis, N., & Rodriguez, D., & Sicilia, M.-A. (2008). The impact of readability on the usefulness of online product reviews: A case study on an online bookstore. In M. D. Lytras et al. (Hrsg.), Emerging technologies and information systems for the knowledge society 2008 (Bd. 5288, S. 423–432). Heidelberg: Springer.

Krafft, M. (1997). Der Ansatz der logistischen Regression und seine Interpretation. Zeitschrift fur Betriebswirtschaft, 67, 625–642.

Krishnamoorthy, S. (2015). Linguistic features for review helpfulness prediction. Expert Systems with Applications, 42, 3751–3759. https://doi.org/10.1016/j.eswa.2014.12.044.CrossRef

Kuan, K., Hui, K.-L., Prasarnphanich, P., & Lai, H.-Y. (2015). What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16, 48–71. https://doi.org/10.17705/1jais.00386.CrossRef

Li, M., Huang, L., Tan, C.-H., & Wei, K.-K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17, 101–136. https://doi.org/10.2753/jec1086-4415170404.CrossRef

Lim, Y., & Van Der Heide, B. (2015). Evaluating the wisdom of strangers: The perceived credibility of online consumer reviews on yelp. Journal of Computer-Mediated Communication, 20(1), 67–82.

Luxmore, J. (2015). AmazonProductAdvertising [WWW Document]. https://github.com/zoenberger/AmazonProductAdvertising. Zugegriffen: 11. Aug. 2019.

MacInnis, D. J., Moorman, C., & Jaworski, B. J. (1991). Enhancing and measuring consumers’ motivation, opportunity, and ability to process brand information from ads. Journal of Marketing, 55, 32. https://doi.org/10.2307/1251955.CrossRef

Molnar, C. (2019). Interpretable machine learning. A guide for making black box models explainable. Morrisville: Lulu.

Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on amazon.com. MIS Quarterly Management Information Systems, 34, 185–200. https://doi.org/10.2307/20721420.CrossRef

Mukherjee, A., Liu, B., & Glance, N. (2012). Spotting fake reviewer groups in consumer reviews. In Proceedings of the 21st international conference on World Wide Web (S. 191). https://doi.org/10.1145/2187836.2187863.

Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311–329. https://doi.org/10.1086/259630.CrossRef

Németh, L. (2014). Hunspell: Open source spell checker [WWW Document]. http://hunspell.github.io/. Zugegriffen: 15. Sept. 2019.

Nyblom, V. (2010). The role of customer magazines [WWW Document]. http://www.emarketer.com/Article/Role-of-Customer-Product-Reviews/1008019. Zugegriffen: 11. Aug. 2019.

O’Mahony, M. P., & Smyth, B. (2009). Learning to recommend helpful hotel reviews. In L. Bergman & A. Tuzhilin (Hrsg.), Proceedings of the third ACM conference on recommender systems (S. 305). https://doi.org/10.1145/1639714.1639774.

O’Mahony, M. P., & Smyth, B. (2010). Using readability tests to predict helpful product reviews. In P. Gabriella (Hrsg.), Adaptivity, personalization and fusion of … (S. 164–167).

Ott, M., Cardie, C., & Hancock, J. (2012). Estimating the prevalence of deception in online review communities. In WWW’12 – Proceedings 21st annual conference World Wide Web (S. 201–210). https://doi.org/10.1145/2187836.2187864.

Otterbacher, J. (2009). Helpfulness in online communities: a measure of message quality. In W. E. Mackay (Hrsg.), Proceedings of the 27th international conference on human factors in computing systems (ACM CHI ’09) (S. 955–964).

Peters, K., & Kashima, Y. (2007). From social talk to social action: Shaping the social triad with emotion sharing. – PsycARTICLES – ProQuest. Journal of Personality and Social Psychology, 93, 780–797.CrossRef

Pliskin, J., & Kennedy, P. (1987). A guide to econometrics. Journal of the American Statistical Association. Cambridge: Blackwell. https://doi.org/10.2307/2288828.

Quoc, H. T., Ochiai, H., & Esaki, H. (2015). Hidden topics modeling approach for review quality prediction and classification. In Proceedings of the 2015 7th international conference of soft computing and pattern recognition, SoCPaR 2015. Tokyo. https://doi.org/10.1109/socpar.2015.7492821.

Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5, 296–320.CrossRef

Schieber, A., Hilbert, A., & Stillich, C. (2012). Identifikation und Analyse von ironischen und sarkastischen Kundenrezensionen im Web. Multikonferenz Wirtschaftsinformatik 2012-Tagungsband der MKWI 2012 1157–1168.

Schindler, R. M., & Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal of Consumer Behaviour, 243, 234–243. https://doi.org/10.1002/cb.1372.CrossRef

Scholz, M., & Dorner, V. (2013). The recipe for the perfect review? Business & Information Systems Engineering, 5, 141–151.CrossRef

Sherman, L. A. (1893). Analytics of literature: A manual for the objective study of english prose and poetry, Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki. Ginn. London: Creative Media Partners.

Siering, M., & Muntermann, J. (2013). What drives the helpfulness of online product reviews? From stars to facts and emotions. In R. Alt, & B. Franczyk (Hrsg.), 11th International Conference on Wirtschaftsinformatik (S. 103–118).

Smith, E. A., & Kincaid, J. P. (1970). Derivation and validation of the automated readability index for use with technical materials. Journal of Human Factors Ergonomics Society, 12, 457–564. https://doi.org/10.1177/001872087001200505.CrossRef

Spool, J. M. (2009). The magic behind Amazon’s 2.7 billion dollar question [WWW Document]. https://articles.uie.com/magicbehindamazon/. Zugegriffen: 11. Aug 2019.

Stone, P. J., Bales, R. F., Namenwirth, J. Z., & Ogilvie, D. M. (1962). The general inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information. Behavioral Science, 7, 484–498.CrossRef

To, G. (2015). Evaluating the wisdom of strangers: The perceived credibility of online. Journal on Computer Communications, 20, 67–82. https://doi.org/10.1111/jcc4.12093.CrossRef

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases, science. American Association for the Advancement of Science, 185, 1124.

Vessey, I. (1991). Cognitive fit: A theory-based analysis of the graphs versus tables literature. Decision Sciences, 22, 219–240.CrossRef

Weathers, D., Swain, S. D., & Grover, V. (2015). Can online product reviews be more helpful? Examining characteristics of information content by product type. Decision Support Systems, 79, 12–23.

Xia, L., & Bechwati, N. N. (2008). Word of mouse: The role of cognitive personalization in online consumer reviews. Journal of Interacting Advertising, 9, 3–13.CrossRef

Title: Analyse der Nützlichkeit von Amazon-Produktbewertungen mittels Text Mining
Authors: Florian Bosten
Manuel Di Stefano
Maren Hartmann
Sebastian Sauer
Rüdiger Buchkremer
Publisher: Springer Fachmedien Wiesbaden
Book: Künstliche Intelligenz in Wirtschaft & Gesellschaft
Print ISBN: 978-3-658-29549-3

Electronic ISBN: 978-3-658-29550-9

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-658-29550-9_30

Springer Professional

Zusammenfassung

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Premium Partner