Abstract
This paper proposes a method for discovering association rules on peoples’ experiences extracted from a large-scale set of blog entries. In our definition, a person’s experience can be expressed by five attributes: time, location, activity, opinion and emotion. The system implementing our proposed method actually generates and ranks association rules between attributes by applying several interestingness measures proposed in the area of data mining to the experiences extracted from 48 million blog entries. An experiment shows that the system successfully mines peoples’ activities and emotions which are specific to location and time period.
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Kurashima, T., Fujimura, K., Okuda, H. (2009). Discovering Association Rules on Experiences from Large-Scale Blog Entries. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_49
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DOI: https://doi.org/10.1007/978-3-642-00958-7_49
Publisher Name: Springer, Berlin, Heidelberg
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