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Research Papers

Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data

[+] Author and Article Information
Suppawong Tuarob

Computer Science and Engineering,
Industrial and Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: suppawong@psu.edu

Conrad S. Tucker

Engineering Design and Industrial Engineering,
Computer Science and Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: ctucker4@psu.edu

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received June 20, 2014; final manuscript received December 11, 2014; published online April 9, 2015. Assoc. Editor: Joshua D. Summers.

J. Comput. Inf. Sci. Eng 15(3), 031003 (Sep 01, 2015) (12 pages) Paper No: JCISE-14-1216; doi: 10.1115/1.4029562 History: Received June 20, 2014; Revised December 11, 2014; Online April 09, 2015

Some of the challenges that designers face in getting broad external input from customers during and after product launch include geographic limitations and the need for physical interaction with the design artifact(s). Having to conduct such user-based studies would require huge amounts of time and financial resources. In the past decade, social media has emerged as an increasingly important medium of communication and information sharing. Being able to mine and harness product-relevant knowledge within such a massive, readily accessible collection of data would give designers an alternative way to learn customers' preferences in a timely and cost-effective manner. In this paper, we propose a data mining driven methodology that identifies product features and associated customer opinions favorably received in the market space which can then be integrated into the design of next generation products. Two unique product domains (smartphones and automobiles) are investigated to validate the proposed methodology and establish social media data as a viable source of large scale, heterogeneous data relevant to next generation product design and development. We demonstrate in our case studies that incorporating suggested features into next generation products can result in favorable sentiment from social media users.

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References

Zhang, K., Narayanan, R., and Choudhary, A., 2010, “Voice of the Customers: Mining Online Customer Reviews for Product Feature-Based Ranking,” 3rd Conference on Online Social Networks, USENIX Association.
Richins, M. L., 1983, “Negative Word-of-Mouth by Dissatisfied Consumers: A Pilot Study,” J. Mark., 47(1), pp. 68–78. [CrossRef]
Tietz, R., Morrison, P. D., Luthje, C., and Herstatt, C., 2005, “The Process of User-Innovation: A Case Study in a Consumer Goods Setting,” Int. J. Prod. Dev., 2(4), pp. 321–338. [CrossRef]
Luthje, C., 2004, “Characteristics of Innovating Users in a Consumer Goods Field: An Empirical Study of Sport-Related Product Consumers,” Technovation, 24(9), pp. 683–695. [CrossRef]
Franke, N., Von Hippel, E., and Schreier, M., 2006, “Finding Commercially Attractive User Innovations: A Test of Lead-User Theory,” J. Prod. Innovation Manage., 23(4), pp. 301–315. [CrossRef]
Tuarob, S., and Tucker, C. S., 2014, “Discovering Next Generation Product Innovations by Identifying Lead User Preferences Expressed Through Large Scale Social Media Data,” ASME Paper No. DETC2014-34767. [CrossRef]
Wu, X., Zhu, X., Wu, G.-Q., and Ding, W., 2014, “Data Mining With Big Data,” IEEE Trans. Knowl. Data Eng., 26(1), pp. 97–107. [CrossRef]
Bodnar, T., Tucker, C., Hopkinson, K., and Bilén, S., 2014, “Increasing the Veracity of Event Detection on Social Media Networks Through User Trust Modeling,” 2014 IEEE International Conference on Big Data, Washington, DC, Oct. 27–30, pp. 636–643. [CrossRef]
IBM, 2013, “What Is Big Data?—Bringing Big Data to the Enterprise,” Available at http://www-01.ibm.com/software/ph/data/bigdata/ [Accessed Aug. 16, 2013].
Sakaki, T., Okazaki, M., and Matsuo, Y., 2010, “Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors,” 19th International Conference on World Wide Web, WWW’10, Raleigh, NC, Apr. 26–30, pp. 851–860. [CrossRef]
Caragea, C., McNeese, N., Jaiswal, A., Traylor, G., Kim, H., Mitra, P., Wu, D., Tapia, A., Giles, L., Jansen, B., and Yen, J., 2011, “Classifying Text Messages for the Haiti Earthquake,” 8th International Conference on Information Systems for Crisis Response and Management (ISCRAM2011), pp. 1–10.
Collier, N., and Doan, S., 2012, “Syndromic Classification of Twitter Messages,” Electronic Healthcare, P.Kostkova, M.Szomszor, and D.Fowler, eds., Vol. 91, Springer, Berlin, Germany, pp. 186–195. [CrossRef]
Bollen, J., Mao, H., and Zeng, X., 2011, “Twitter Mood Predicts the Stock Market,” J. Comput. Sci., 2(1), pp. 1–8. [CrossRef]
Esparza, S. G., O'Mahony, M. P., and Smyth, B., 2012, “Mining the Real-Time Web: A Novel Approach to Product Recommendation,” Knowl. Based Syst., 29, pp. 3–11. [CrossRef]
Tucker, C., and Kim, H., 2011, “Trend Mining for Predictive Product Design,” ASME J. Mech. Des., 133(11), p. 111008. [CrossRef]
Kaplan, A. M., and Haenlein, M., 2010, “Users of the World, Unite! The Challenges and Opportunities of Social Media,” Bus. Horiz., 53(1), pp. 59–68. [CrossRef]
Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., and Ghosh, R., 2013, “Exploiting Burstiness in Reviews for Review Spammer Detection,” Seventh International AAAI Conference on Weblogs and Social Media, pp. 175–184.
Chevalier, J. A., and Mayzlin, D., 2006, “The Effect of Word of Mouth on Sales: Online Book Reviews,” J. Mark. Res., 43(3), pp. 345–354. [CrossRef]
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., and Silvestre, B. S., 2011, “Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media,” Bus. Horiz., 54(3), pp. 241–251. [CrossRef]
Himelboim, I., McCreery, S., and Smith, M., 2013, “Birds of a Feather Tweet Together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter,” J. Comput. Mediated Commun., 18(2), pp. 40–60. [CrossRef]
Dellarocas, C., 2003, “The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms,” Manage. Sci., 49(10), pp. 1407–1424. [CrossRef]
Fuge, M., Tee, K., Agogino, A., and Maton, N., 2014, “Analysis of Collaborative Design Networks: A Case Study of Openideo,” ASME J. Comput. Inf. Sci. Eng., 14(2), p. 021009. [CrossRef]
Yassine, A. A., and Bradley, J. A., 2013, “A Knowledge-Driven, Network-Based Computational Framework for Product Development Systems,” ASME J. Comput. Inf. Sci. Eng., 13(1), p. 011005. [CrossRef]
Liu, Y., Liang, Y., Kwong, C. K., and Lee, W. B., 2010, “A New Design Rationale Representation Model for Rationale Mining,” ASME J. Comput. Inf. Sci. Eng., 10(3), p. 031009. [CrossRef]
Lim, S. C. J., Liu, Y., and Loh, H. T., 2012, “An Exploratory Study of Ontology-Based Platform Analysis Under User Preference Uncertainty,” ASME Paper No. DETC2012-70756. [CrossRef]
Tucker, C. S., and Kim, H. M., 2009, “Data-Driven Decision Tree Classification for Product Portfolio Design Optimization,” ASME J. Comput. Inf. Sci. Eng., 9(4), p. 041004. [CrossRef]
Tucker, C., and Kim, H., 2011, “Predicting Emerging Product Design Trend by Mining Publicly Available Customer Review Data,” 18th International Conference on Engineering Design (ICED11), Vol. 6, pp. 43–52.
Ghani, R., Probst, K., Liu, Y., Krema, M., and Fano, A., 2006, “Text Mining for Product Attribute Extraction,” SIGKDD Explor. Newsl., 8(1), pp. 41–48. [CrossRef]
Putthividhya, D. P., and Hu, J., 2011, “Bootstrapped Named Entity Recognition for Product Attribute Extraction,” Conference on Empirical Methods in Natural Language Processing, EMNLP’11, Stroudsburg, PA, pp. 1557–1567.
Popescu, A.-M., and Etzioni, O., 2005, “Extracting Product Features and Opinions From Reviews,” Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT’05, Stroudsburg, PA, pp. 339–346. [CrossRef]
Tuarob, S., Tucker, C. S., Salathe, M., and Ram, N., 2014, “An Ensemble Heterogeneous Classification Methodology for Discovering Health-Related Knowledge in Social Media Messages,” J. Biomed. Inform., 49, pp. 255–268. [CrossRef] [PubMed]
Asur, S., and Huberman, B. A., 2010, “Predicting the Future With Social Media,” 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT’10, Vol. 1, Toronto, ON, Aug. 31–Sept. 3, pp. 492–499. [CrossRef]
Wang, L., Youn, B., Azarm, S., and Kannan, P., 2011, “Customer-Driven Product Design Selection Using Web Based User-Generated Content,” ASME Paper No. DETC2011-48338. [CrossRef]
Tuarob, S., and Tucker, C. S., 2013, “Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data,” ASME Paper No. DETC2013-12661. [CrossRef]
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., and Kappas, A., 2010, “Sentiment in Short Strength Detection Informal Text,” J. Am. Soc. Inf. Sci. Technol., 61(12), pp. 2544–2558. [CrossRef]
Fox, E., 2008, Emotion Science: Cognitive and Neuroscientific Approaches to Understanding Human Emotions, Palgrave Macmillan, Basingstoke, New York.
Thelwall, M., 2013, “Heart and Soul: Sentiment Strength Detection in the Social Web With SentiStrength,” Cyberemotions, pp. 1–14.
Babich, P., 1992, “Customer Satisfaction: How Good is Good Enough?” Qual. Prog., 25, pp. 65–67.
Manning, C. D., Raghavan, P., and Schütze, H., 2008, Introduction to Information Retrieval, Vol. 1, Cambridge University Press, Cambridge, UK.
Huang, J., Etzioni, O., Zettlemoyer, L., Clark, K., and Lee, C., 2012, “Revminer: An Extractive Interface for Navigating Reviews on a Smartphone,” 25th Annual ACM Symposium on User Interface Software and Technology, UIST’12, New York, pp. 3–12. [CrossRef]
Gimpel, K., Schneider, N., O'Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., and Smith, N. A., 2011, “Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments,” 49th Annual Meeting of the ACL: HLT 2011, Stroudsburg, PA, pp. 42–47.
Blei, D. M., Ng, A. Y., and Jordan, M. I., 2003, “Latent Dirichlet Allocation,” J. Mach. Learn. Res., 3, pp. 993–1022.
Thelen, M., and Riloff, E., 2002, “A Bootstrapping Method for Learning Semantic Lexicons Using Extraction Pattern Contexts,” ACL-02 Conference on Empirical Methods in Natural Language Processing, EMNLP’02, Vol. 10, Stroudsburg, PA, pp. 214–221. [CrossRef]
Asuncion, A., Welling, M., Smyth, P., and Teh, Y. W., 2009, “On Smoothing and Inference for Topic Models,” Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI’09, Arlington, VA, pp. 27–34.
Tuarob, S., Bhatia, S., Mitra, P., and Giles, C., 2013, “Automatic Detection of Pseudocodes in Scholarly Documents Using Machine Learning,” 12th International Conference on Document Analysis and Recognition (ICDAR), Washington, DC, Aug. 25–28, pp. 738–742. [CrossRef]
Pookulangara, S., and Koesler, K., 2011, “Cultural Influence on Consumers' Usage of Social Networks and Its' Impact on Online Purchase Intentions,” J. Retailing Consum. Serv., 18(4), pp. 348–354. [CrossRef]
Ioanăs, E., and Stoica, I., 2014, “Social Media and Its Impact on Consumers Behavior,” Int. J. Econ. Pract. Theor., 4(2), pp. 295–303.
Huang, E. H., Socher, R., Manning, C. D., and Ng, A. Y., 2012, “Improving Word Representations Via Global Context and Multiple Word Prototypes,” ACL’12, pp. 873–882.

Figures

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Fig. 1

Proposed method for quantifying PF and product features

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Fig. 2

Comparison between the PF score versus GSMArea daily interest for each sample smartphone model (in log scale). The products are ordered by their Favorability scores.

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Fig. 3

Comparison between the Favorability score versus U.S. News and consumer reports ratings for each sample automobile model. The models are ordered by their Favorability scores.

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Fig. 4

Comparison between the positive and negative sentiments related to some features of iPhone 4 and iPhone 5

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Fig. 5

Comparison between the positive and negative sentiments related to some features of Samsung Galaxy S II and Samsung Galaxy S III

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Fig. 6

Sample feature opinions related to the iPhone 4, arranged in hierarchy of Product Name → Feature → Opinion → Snippets

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