Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Using Implicit Preference Relations to Improve Content Based Recommending Read chapter Read first chapter

2015 | OriginalPaper | Chapter

The Interactive Effect of Review Rating and Text Sentiment on Review Helpfulness

Authors : Shasha Zhou, Bin Guo

Published in: E-Commerce and Web Technologies

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Review ratings and text sentiments respectively represent quantitative and qualitative aspects of user-generated product reviews. These two types of polarity information complement each other in affecting consumers’ review evaluation. Few extant studies consider the interplay of review rating and text sentiment on perceived review helpfulness. In this study, we attempt to investigate this potential interaction effect and examine whether it is conditional on review length. The empirical results from an analysis of 70,610 restaurant reviews from Yelp.com indicate that both review ratings and text sentiments exhibit negativity bias effect, such that negative ratings and texts are more helpful than positive ones. Meanwhile, the two types of review valence have a positive interaction effect on perceived review helpfulness. Moreover, the interaction effect of review rating and text sentiment is stronger for longer reviews.

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 "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

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 Exploiting Microdata Annotations to Consistently Categorize Product Offers at Web Scale
next chapter A Twitter View of the Brazilian Stock Exchange Market
Literature
1.
Yin, D., Bond, S., Zhang, H.: Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Q. 38(2), 539–560 (2014)
2.
Chevalier, J.A., Mayzlin, D.: The effect of word of mouth on sales: online book reviews. J. Mark. Res. 43(3), 345–354 (2006)CrossRef
3.
Duan, W., Gu, B., Whinston, A.B.: The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry. J. Retail. 84(2), 233–242 (2008)CrossRef
4.
Forman, C., Ghose, A., Wiesenfeld, B.: Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Inf. Syst. Res. 19(3), 291–313 (2008)CrossRef
5.
Purnawirawan, N., De Pelsmacker, P., Dens, N.: Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. J. Interact. Mark. 26(4), 244–255 (2012)CrossRef
6.
Park, D.-H., Lee, J., Han, I.: The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. Int. J. Electron. Commer. 11(4), 125–148 (2007)CrossRef
7.
Cao, Q., Duan, W., Gan, Q.: Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decis. Support Syst. 50(2), 511–521 (2011)CrossRef
8.
Mudambi, S.M., Schuff, D.: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS Q. 34(1), 185–200 (2010)
9.
Schlosser, A.E.: Can including pros and cons increase the helpfulness and persuasiveness of online reviews? The interactive effects of ratings and arguments. J. Consum. Psychol. 21(3), 226–239 (2011)CrossRef
10.
Schindler, R.M., Bickart, B.: Perceived helpfulness of online consumer reviews: The role of message content and style. J. Consum. Behav. 11(3), 234–243 (2012)CrossRef
11.
Racherla, P., Friske, W.: Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electron. Comme. Res. Appl. 11(6), 548–559 (2012)CrossRef
12.
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. Electron. Commer. Res. Appl. 11(3), 205–217 (2012)CrossRef
13.
Pan, Y., Zhang, J.Q.: Born unequal: A study of the helpfulness of user-generated product reviews. J. Retail. 87(4), 598–612 (2011)CrossRef
14.
Sen, S., Lerman, D.: Why are you telling me this? An examination into negative consumer reviews on the web. J. Interact. Mark. 21(4), 76–94 (2007)CrossRef
15.
Willemsen, L.M., Neijens, P.C., Bronner, F., de Ridder, J.A.: Highly recommended! the content characteristics and perceived usefulness of online consumer reviews. J. Comput.-Mediated Commun. 17(1), 19–38 (2011)CrossRef
16.
Yang, J., Mai, E.S.: Experiential goods with network externalities effects: An empirical study of online rating system. J. Bus. Res. 63(9), 1050–1057 (2010)CrossRef
17.
Ito, T.A., Larsen, J.T., Smith, N.K., Cacioppo, J.T.: Negative information weighs more heavily on the brain: The negativity bias in evaluative categorizations. J. Pers. Soc. Psychol. 75(4), 887–900 (1998)CrossRef
18.
Ghose, A., Ipeirotis, P.G.: Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. 23(10), 1498–1512 (2011)CrossRef
19.
Hu, N., Koh, N.S., Reddy, S.K.: Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decis. Support Syst. 57, 42–53 (2014)CrossRef
20.
Tsang, A.S., Prendergast, G.: Is a “star” worth a thousand words? The interplay between product-review texts and rating valences. Eur. J. Mark. 43(11/12), 1269–1280 (2009)CrossRef
21.
Bakhshi, S., Kanuparthy, P., Shamma, D.A.: Understanding online reviews: funny, cool or useful? In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 1270–1276. ACM, New York (2015)
22.
Anderson, R.E.: Consumer dissatisfaction: The effect of disconfirmed expectancy on perceived product performance. J. Mark. Res. 10(1), 38–44 (1973)CrossRef
23.
Chen, Z., Lurie, N.H.: Temporal contiguity and negativity bias in the impact of online word-of-mouth. J. Mark. Res. 50(4), 463–476 (2013)CrossRef
24.
Pennebaker, J.W., Chung, C.K., Ireland, M., Gonzales, A., Booth, R.J.: The development and psychometric properties of LIWC2007. LIWC.net, Austin, TX (2007)
25.
Ludwig, S., de Ruyter, K., Friedman, M., Brüggen, E.C., Wetzels, M., Pfann, G.: More than words: the influence of affective content and linguistic style matches in online reviews on conversion rates. J. Mark. 77(1), 87–103 (2013)CrossRef
26.
Aiken, L.S., West, S.G.: Multiple regression: Testing and interpreting interactions. Sage Publications, Thousand Oaks (1991)
27.
Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C.: Multivariate data analysis, 5th edn. Prentice-Hall, Eenglewood, Cliffs, NJ (1998)
28.
Cohen, J., Cohen, P., West, S.G., Aiken, L.S.: Applied multiple regression/correlation analysis for the behavioral sciences, 3rd edn. Lawrence Erlbaum Associates, New Jersey (2003)
29.
Godes, D., Silva, J.C.: Sequential and temporal dynamics of online opinion. Mark. Sci. 31(3), 448–473 (2012)CrossRef
Metadata
Title
The Interactive Effect of Review Rating and Text Sentiment on Review Helpfulness
Authors
Shasha Zhou
Bin Guo
Copyright Year
2015
Book
E-Commerce and Web Technologies

Print ISBN: 978-3-319-27728-8

Electronic ISBN: 978-3-319-27729-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-27729-5_8

Premium Partner

    Image Credits