Needs-based analysis of online customer reviews

ACM International Conference Proceeding Series

Volumn 258, Issue , 2007, Pages 311-318

  Lee, Thomas Y ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Customer needs; Customer reviews; Text mining; User needs

Indexed keywords

CUSTOMER NEEDS; CUSTOMER REVIEWS; TEXT MINING; USER NEEDS;

DATA MINING; ELECTRONIC COMMERCE; PRODUCT DEVELOPMENT; TEXT PROCESSING;

CUSTOMER SATISFACTION;

EID: 36849034884     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1282100.1282159     Document Type: Conference Paper

References (37)

1. Adomavicius, G. and Tuzhilin, A. Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions. IEEE Transactions of Knowledge and Data Engineering, 17 (6). 734-749.
2. Borgelt, C. and Kruse, R., Induction of Association Rules: Apriori Implementation, in 15th Conf on Computational Statistics (Compstat), (Berlin, Germany, 2002).
3. Chen, Y. and Xie, J. Online Customer Review: A New Element of Marketing Communications Mix SSRN, 2004.
4. Cheung, K.-W., Kwok, J.T., Law, M.H. and Tsui, K.-C. Mining customer product ratings for personalized marketing. Decision Support Systems, 35. 231-243.
5. Chevalier, J. A. and Mayzlin, D. The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research, 43. 345-354.
6. Coates, S. Consumer Protection Laws Times of London, London, UK, 2007.
7. Dave, K., Lawrence, S. and Pennock, D.M., Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews, in WWW, (2003), 519-528.
8. Ghose, A., Ipeirotis, P. and Sundararajan, A. The Dimensions of Reputation in Electronic Markets, New York University, 2006, 32.
9. Grenci, R.T. and Todd, P.A. Solutions-Driven Marketing. Communicatons of the ACM, 45 (3). 65-71.
10. Han, E.-H, Karypis, G., Kumar, V. and Mobasher, B., Clustering Based on Association Rule Hypergraphs. in Workshop on Research Issues in Data Mining and Knowledge Discovery, (1997).
11. Han, J. and Kamber, M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco, 2001.
12. HOG. Harley-Davidson Owner's Group.
13. Hu, M. and Liu, B., Mining and Summarizing Customer Reviews. in KDD04, (Seattle, WA, 2004), ACM.
14. Kotler, P. Marketing Management. Prentice Hall, 2003.
15. Lee, T., Use-centric mining of customer reviews. in Workshop on Information Technology and Systems (WITS), (Washington, D.C., 2004), 146-151.
16. Lee, T.Y. Constraint-based Ontology Induction from Online Customer Reviews. Group Decision and Negotiation, 16. 255-281.
17. Liu, B., Hu, M. and Cheng, J., Opinion Observer: Analyzing and Comparing Opinons on the Web. in WWW 2005, (Chiba, Japan, 2005), ACM.
18. Liu, H. MontyLingua: An End-to-End Natural Language Processor with Common Sense, 2004.
19. McEachern, T. and O'Keefe, B. Re-Wiring Business: Uniting Management and the Web. John Wiley & Sons, Inc., New York, 1998.
20. Nasukawa, T. and Yi, J., Sentiment Analysis: Capturing Favorability Using Natural Language Processing, in KCAP"03, (Sanibel Island, Florida, 2003), ACM, 70-77.
21. O'Keefe, R.H. and McEachern, T. Web-based Customer Decision Support Systems. Communicatons of the ACM, 41 (3). 71-78.
22. OED. Oxford English Dictionary.
23. Orman, L. Consumer Support Systems. Communicatons of the ACM, 50 (4). 49-54.
24. Popescu, A.-M. and Etzioni, O., Extracting Product Features and Opinions from Reviews. in HLT-EMNLP, (2005).
25. Popping, R. Computer-Assisted Text Analysis. Sage Publications, Thousand Oaks, CA, 2000.
26. Randall, T., Terwiesch, C. and Ulrich, K.T. User Design of Customized Products. Marketing Science, 26 (2). 268-280.
27. Rich, E. and Knight, K. Artificial Intelligence. McGraw-Hill, New York, 1991.
28. Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H. and Jin, C., Red Opal: Product-Feature Scoring from Reviews. in ACM EC, (San Diego, CA, 2007).
29. Skiena, S. The Algorithm Design Manual. TELOS, SpringerVerlag, New York, 1998.
30. Stephens, C. Embracing Consumer Buzz Creates Measurement Challenges for Marketers Spark Research, Compete, Inc., Boston, 2006.
31. Stolze, M. and Strobel, M. Recommending as Personalized Teaching. in Karat, C.-M., Blom, J.O. and Karat, J. eds. Designing Personalized User Experiences in eCommerce, Springer, Netherlands, 2004, 293-313.
32. Toubia, O., Simester, D.I., Houser, J.R. and Dahan, E. Fast Polyhederal Adaptive Conjoint Estimation. Marketing Science, 22 (3). 273-303.
33. Turney, P., Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews, in, (2002), 417-424.
34. Ulrich, K.T. and Eppinger, S.D. Product Design and Development. McGraw-Hill/Irwin, New York, 2003.
35. Urban, G.L. and Hauser, J.R. "Listening In" to Find Unment Customer Needs and Solutions eBusiness@MIT, no. 156, MIT, Boston, MA, 2003, 36.
36. Weber, B. Basic Content Analysis. Sage Publications, Newbury Park, CA, 1990.
37. Yahoo and comScore. Engaging Advocates through Search and Social Media, 2006.