Abstract
As the number of websites which sell products online increases, the number of people who buy products using such websites has also increased. People cannot actually check out products detail when they buy products online, while people who buy products offline can do. Therefore, opinion articles written by other people are increasingly important, and demand for adequate search systems for finding and retrieving meaningful online opinion articles has increased. In this paper, we briefly describe our proper retrieval system for searching opinion articles and illustrate how crawler of our system can collect documents efficiently. Moreover, we propose the improved ranking technique adopted by our system for opinion article retrieval. We also show that our ranking technique outperform existing ranking techniques in retrieving opinion articles.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 2012-0003740 and 2012-0000478).
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Amazon: Online bookstore, http://www.amazon.com
Facebook: Social Network Service, http://www.facebook.com
GoodReads: Social network for readers, http://www.goodreads.com
Google: Search engine for internet information, http://www.google.com
Twitter: Social Network Service, http://www.twitter.com
Yahoo: Search engine for internet information, http://www.yahoo.com
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Yun, U., Ryang, H., Pyun, G., Lee, G. (2012). Efficient Opinion Article Retrieval System. In: Lee, G., Howard, D., Kang, J.J., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Lecture Notes in Computer Science, vol 7425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32645-5_71
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DOI: https://doi.org/10.1007/978-3-642-32645-5_71
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