Customer Feedback System - Evolution towards Semantically-enhanced Systems

Oleksiy Khriyenko


The digital economy requires services be created in nearly real time – while continuously listening to the customer. Managing and analysing the data collected about products and customers become very critical. Successful companies must collect data regarding customer behaviour in a sensible manner, understand their customers and engage in constant interaction with them. Nowadays, having a huge data storage capacity, everyone collects data and hopes that it will be useful someday. But, it is frustrating when you do not know whether something useful will come out of it. It is not a problem to collect data, but it is very difficult to analyse it. To utilize the data they collect and analyse customer feedback quickly, companies require automation of customer feedback processing. To hear a real voice of a customer, companies are trying to engage customer to the feedback provisioning process. Therefore, the paper reviews digitalized customer feedback strategies, highlights challenges of a feedback gathering and further computation. As a result, paper presents an approach for semantic enhancement of a customer feedback system.


  1. Agathangelou P., Katakis I., Kokkoras F., Ntonas K., 2014. Mining Domain-Specific Dictionaries of Opinion Words, In 15th International Conference on Web Information System Engineering (WISE 2014), Thessaloniki, Greece, 12-14 October, 2014.
  2. Allen J.F., Swift M., Beaumont W., 2008. Deep semantic analysis of text, Proceedings of the Conference on Semantics in Text Processing, p.343-354, September 22-24, 2008, Venice, Italy.
  3. Berners-Lee T., Hendler J., Lassila O., 2001. “The Semantic Web”, Scientific American 284(5), pp.34-43.
  4. Cimiano P., Ladwig G., Staab S., 2005. Gimme' the context: context-driven automatic semantic annotation with c-pankow. In WWW 7805, pages 332-341, NY, USA, 2005. ACM Press. ISBN 1-59593-046-9.
  5. Cherfi, H., Corby, O., Faron-Zucker, C., Khelif, K., Nguyen, M. T., 2008. Semantic Annotation of Texts with RDF Graph Contexts. In ICCS Supplement.
  6. Dave, K., Lawrence, S., Pennock, D., 2003. Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. WWW'03.
  7. Gamon M., 2004. Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of the 20th international conference on Computational Linguistics. Association for Computational Linguistics, 2004, p. 841.
  8. Jain P., Hitzler P., Sheth A.P., Verma K., Yeh P.Z., 2010. “Ontology Alignment for Linked Open Data”. In: Proceedings of the 9th International SemanticWeb Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Springer-Verlag, 402-417.
  9. Jurafsky D., Martin J.H., 2000. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Prentice Hall PTR, Upper Saddle River, NJ, 2000.
  10. Laclavík, M., Maynard, D., 2009. Motivating Intelligent Email in Business: An Investigation Into Current Trends for Email Processing and Communication Research. In E3C Workshop; IEEE Conference on Commerce and Enterprise Computing; pp. 476-482.
  11. Liu B., Hu M., Cheng J., 2005. Opinion observer: analyzing and comparing opinions on the web. In proceedings of the 14th international conference on World Wide Web, Chiba, Japan, May 10 - 14, 2005, ACM Press New York, NY, 342-351.
  12. Mcdonald J., Knott A., Zeng R. 2012. Free-text input vs menu selection: exploring the difference with a tutorial dialogue system. In proceedings of the Australasian Language Technology Association Workshop. Dunedin, New Zealand, December 2012, pp.97-105.
  13. Semantic Web, 2001. URL: Shvaiko P., Euzenat J., 2012. Ontology matching: state of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering, 2012.
  14. Sreerama K., 1998. Murthy, Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery, v.2 n.4, p.345-389, December 1998.
  15. Zavitsanos E., Tsatsaronis G., Varlamis I., Paliouras G., 2010. Scalable semantic annotation of text using lexical and web resources. In Artificial Intelligence: Theories, Models and Applications (pp. 287-296). Springer Berlin Heidelberg.

Paper Citation

in Harvard Style

Khriyenko O. (2015). Customer Feedback System - Evolution towards Semantically-enhanced Systems . In Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-106-9, pages 518-525. DOI: 10.5220/0005480505180525

in Bibtex Style

author={Oleksiy Khriyenko},
title={Customer Feedback System - Evolution towards Semantically-enhanced Systems},
booktitle={Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Customer Feedback System - Evolution towards Semantically-enhanced Systems
SN - 978-989-758-106-9
AU - Khriyenko O.
PY - 2015
SP - 518
EP - 525
DO - 10.5220/0005480505180525