AFFECTIVE ALGORITHM TO POLARIZE CUSTOMER OPINIONS

Domenico Consoli, Claudia Diamantini, Domenico Potena

Abstract

Human interact with other people and exchange reviews and ideas via web. With the explosion of Web 2.0 platforms such as blogs, discussion forums, peer-to-peer networks, and various other types of social media, consumers share, on the web, their opinions regarding any product/service. Opinions give information about how product/service and reality in general is perceived by other people. Emotional needs are associated with the psychological aspects of product ownership. The customer when writes his reviews on a product/service transmits emotions in the message that he/she feels first and after purchasing the product. For the enterprise understanding customer emotional needs is vital for predicting and influencing their purchasing behaviour. In this paper, we polarize, with original algorithm, customer opinions basing on emotional indexes that are used for decipher, in affective key, facial expressions and emotional lexicon.

References

  1. Berry, M. and Castellanos, M., editors (2007). Survey of Text Mining II: Clustering, Classification, and Retrieval. Springer.
  2. Boucouvalas, A. and Zhe, X. (2002). Text-to emotion engine for real time internet communication. In Proceedings of International Symposium on CSNDSP, pages 164-168, UK. Staffordshire University.
  3. Consoli, D., Diamantini, C., and Potena, D. (2008). Improving the customer intelligence with customer enterprise customer model. In Proceedings of the 10th International Conference on Enterprise Information Systems, pages 323-326, Barcelona, Spain.
  4. Ekman, P. (2007). Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. OWL Books, NY.
  5. Esuli, A. and Sebastiani, F. (2006). SENTIWORDNET: A publicly available lexical resource for opinion mining. In Proceedings of LREC-06, 5th Conference on Language Resources and Evaluation, pages 417-422, Genova, IT.
  6. Khashman, A. (2008). A modified backpropagation learning algorithm with added emo-tional coefficients. In Transaction on neural networks, volume 19 of 11, pages 1896-1909. IEEE.
  7. Liu, H., Lieberman, H., and Selker, T. (2003). A model of textual affect sensing using real-world knowledge. In IUI 7803: Proceedings of the 8th international conference on Intelligent user interfaces, pages 125-132, New York, NY, USA. ACM.
  8. Oks?a, G. and Vajters?ic, M. (2001). Systolic block-jacobi svd algorithm for processor meshes. 185:211-235.
  9. Picard, R. W. (1997). Affective computing. Cambridge, MA, USA.
  10. Strapparava, C. and Valitutti, A. (2004). Wordnet-affect: An affective extension of wordnet. In The 4th International Conference On Language Resources and Evaluation, pages 1083-1086.
  11. Valitutti, A., Strapparava, C., and Stock, O. (2004). Developing affective lexical resources. 2(1):61-83.
Download


Paper Citation


in Harvard Style

Consoli D., Diamantini C. and Potena D. (2009). AFFECTIVE ALGORITHM TO POLARIZE CUSTOMER OPINIONS . In Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 5: ICEIS, ISBN 978-989-8111-88-3, pages 157-160. DOI: 10.5220/0001851601570160


in Bibtex Style

@conference{iceis09,
author={Domenico Consoli and Claudia Diamantini and Domenico Potena},
title={AFFECTIVE ALGORITHM TO POLARIZE CUSTOMER OPINIONS},
booktitle={Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 5: ICEIS,},
year={2009},
pages={157-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001851601570160},
isbn={978-989-8111-88-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Enterprise Information Systems - Volume 5: ICEIS,
TI - AFFECTIVE ALGORITHM TO POLARIZE CUSTOMER OPINIONS
SN - 978-989-8111-88-3
AU - Consoli D.
AU - Diamantini C.
AU - Potena D.
PY - 2009
SP - 157
EP - 160
DO - 10.5220/0001851601570160