Abstract
The aim of this paper is to investigate the role of emotion features in diversifying document rankings to improve the effectiveness of Information Retrieval (IR) systems. For this purpose, two approaches are proposed to consider emotion features for diversification, and they are empirically tested on the TREC 678 Interactive Track collection. The results show that emotion features are capable of enhancing retrieval effectiveness. © 2011 Springer-Verlag.
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CITATION STYLE
Moshfeghi, Y., Zuccon, G., & Jose, J. M. (2011). Using emotion to diversify document rankings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6931 LNCS, pp. 337–341). https://doi.org/10.1007/978-3-642-23318-0_34
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