Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews

Abstract

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

Keywords

attributes customer satisfaction latent Dirichlet allocation online reviews text analysis

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References

Akerlof

G. A.

(1970). The market for “lemons”: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84, 488–500.

Barr

D. J.

Levy

Scheepers

Tily

H. J.

(2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255–278.

Blei

D. M.

A. Y.

Edu

J. B.

(2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

Breazeale

(2009). FORUM: Word of mouse. International Journal of Market Research, 51(3), 1–19.

Cabosky

(2016). Social media opinion sharing: beyond volume. Journal of Consumer Marketing, 33(3), 172–181.

Calantone

R. J.

Chan

Cui

A. S.

(2006). Decomposing product innovativeness and its effects on new product success. The Journal of Product Innovation Management, 23, 408–421.

Chevalier

J. A.

Mayzlin

(2006). The effects of word of mouth on sales: Online book review. Journal of Marketing Research, 43, 345–354.

Danneels

Kleinschmidt

E. J.

(2000). Product innovativeness from the firm’s perspective: Its dimensions and their impact on project selection and performance. The Journal of Product Innovation Management, 18, 357–373.

Fan

Z.-P.

Che

Y.-J.

Chen

Z.-Y.

(2017). Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, 90–100.

10.

Fang

Zhan

(2015). Sentiment analysis using product review data. Journal of Big Data, 2, Article 5.

11.

Farris

Bendle

N. T.

Pfeifer

P. E.

Reibstein

D. J.

(2010). Marketing metrics: The definitive guide to measuring marketing performance. Upper Saddle River: Wharton School Publishing.

12.

Floyd

Freling

Alhoqail

Cho

H. Y.

Freling

(2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90, 217–232.

13.

Fornell

(1992). A national customer satisfaction barometer: The Swedish experience. Journal of Marketing, 56, 6–21.

14.

Guo

Barnes

S. J.

Jia

(2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using Latent Dirichlet Allocation. Tourism Management, 59, 467–483.

15.

Hayes

A. F.

Preacher

K. J.

(2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, 67, 451–470.

16.

Heng

Gao

Jiang

Chen

(2018). Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42, 161–168.

17.

Hennig-Thurau

Gwinner

K. P.

Walsh

(2003). Electronic Word of Mouth: Motives for and consequences of reading customer articulations on the internet. International Journal of Electronic Commerce, 8, 51–74.

18.

Hofacker

C. F.

Malthouse

E. C.

Sultan

(2016). Big Data and consumer behavior: Imminent opportunities. Journal of Consumer Marketing, 33, 89–97.

19.

Sian Koh

Reddy

K. S.

(2014). Ratings lead you to the product, reviews help you clinch it? The dynamics and impact of online review sentiments on products sales. Decision Support Systems, 57, 42–53.

20.

Jia

(2019). Toward a better fitness club: Evidence from exerciser online rating and review using Latent Dirichlet Allocation and support vector machine. International Journal of Market Research, 61, 64–76.

21.

Johns

Pine

(2002). Consumer behaviour in the food service industry: A review. Hospitality Management, 21, 119–134.

22.

Keller

K. L.

(1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1–22.

23.

Lee

J. H.

Jung

S. H.

Park

(2017). The role of entropy of review text sentiments on online WOM and movie box office sales. Electronic Commerce Research and Applications, 22, 42–52.

24.

Mai

(2018). The effect of online reviews on product sales: A joint sentiment-topic analysis. Information & Management, 56, 172–184. doi:10.1016/j.im.2018.04.007

25.

Luca

(2011). Reviews, reputation, and revenue: The case of yelp.com. Retrieved from https://www.hbs.edu/faculty/Publication%20Files/12-016_a7e4a5a2-03f9-490d-b093-8f951238dba2.pdf

26.

Montoya

R. M.

Horton

R. S.

Vevea

J. L.

Citkowicz

Lauber

E. A.

(2010). A re-examination of the mere exposure effect: The influence of repeated exposure on recognition, familiarity, and liking. Psychological Bulletin, 143, 459–498.

27.

Namazi

N.-R.

(2016). Conceptual analysis of moderator and mediator variables in business research. Procedia Economics and Finance, 36, 540–554. doi:10.1016/S2212-5671(16)30064-8

28.

Ngo

L. V.

O’Cass

(2013). Innovation and business success: The mediating role of customer participation. Journal of Business Research, 66, 1134–1142.

29.

Olson

E. M.

Walker

O. C.

Ruekert

R. W.

(1995). Organizing for effective new product development: The moderating role of product. Journal of Marketing, 59, 48–62.

30.

Park

Nicolau

J. L.

(2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67–83.

31.

Pee

L. G.

(2016). Negative online consumer reviews. International Journal of Market Research, 58, 545–567.

32.

Poria

Cambria

Gelbukh

(2016). Knowledge-based systems aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108, 42–49.

33.

Ramanathan

(2011). Guests’ perceptions on factors influencing customer loyalty. International Journal of Contemporary Hospitality Management, 23, 7–25.

34.

Reimer

Benkenstein

(2016). When good WOM hurts and bad WOM gains: The effect of untrustworthy online reviews. Journal of Business Research, 69, 5993–6001.

35.

Rindfleisch

Jeffrey

(1998). Explaining the familiarity-liking relationship: Mere exposure, information availability, or social desirability? Marketing Letters, 9, 5–19.

36.

Robson

Farshid

Bredican

Humphrey

(2013). Making sense of online consumer reviews: A methodology. International Journal of Market Research, 55, 521–537.

37.

Rodale

J. I.

(1986). The synonym finder. Emmaus, PA: Rodale Press.

38.

Ruiz-Mafe

Chatzipanagiotou

Curras-Perez

(2018). The role of emotions and conflicting online reviews on consumers’ purchase intentions. Journal of Business Research, 89, 336–344.

39.

Salehan

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.

40.

Sharma

Baoku

(2013). Customer satisfaction in Web 2.0 and information technology development. Information Technology & People, 26, 347–367.

41.

Singh

J. P.

Irani

Rana

N. P.

Dwivedi

Y. K.

Saumya

Roy

P. K.

(2017). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346–355.

42.

Soriano

R. D.

(2002). Customers’ expectations factors in restaurants: The situation in Spain. International Journal of Quality & Reliability Management, 19, 1055–1067.

43.

Spence

(1973). Job market signaling. The Quarterly Journal of Economics, 87, 355–374.

44.

Srivastava

Sharma

R. W.

(2017). Developing a model for studying the antecedents and effects of Word of Mouth (WoM) and e-WoM marketing based on literature review. Jindal Journal of Business Research, 6, 25–43.

45.

Thelwall

Buckley

(2013). Topic-based sentiment analysis for the social web: The role of mood and issue related words. Journal of the American Society for Information Science and Technology, 64, 1608–1617.

46.

Thelwall

Buckley

Paltoglou

Cai

Kappas

(2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61, 2544–2558.

47.

Tirunillai

Tellis

G. J.

(2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using Latent Dirichlet Allocation. Journal of Marketing Research, 51, 463–479.

48.

van Trijp

H. C.M

van Kleef

(2008). Newness, value and new product performance. Trends in food science & technology, 19(11), 562–573.

49.

Vermeulen

I. E.

Seegers

(2009). Tried and tested: The impact of online hotel reviews on consumer consideration. Tourism Management, 30, 123–127.

50.

Weinberg

B. D.

(2001). Research in exploring the online consumer experience. ACR North American Advances, 28, 227–232.

51.

Zajonc

R. B.

(1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology Monographs, 9, 1–27.

52.

Zhao

Han

Meng

Zhang

(2017). Learning semantic representation from restaurant reviews: A study of Yelp dataset. Retrieved from http://memray.me/uploads/iconference17-representation-yelp.pdf