Indices of novelty for emerging topic detection

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Emerging topic detection is a vital research area for researchers and scholars interested in searching for and tracking new research trends and topics. The current methods of text mining and data mining used for this purpose focus only on the frequency of which subjects are mentioned, and ignore the novelty of the subject which is also critical, but beyond the scope of a frequency study. This work tackles this inadequacy to propose a new set of indices for emerging topic detection. They are the novelty index (NI) and the published volume index (PVI). This new set of indices is created based on time, volume, frequency and represents a resolution to provide a more precise set of prediction indices. They are then utilized to determine the detection point (DP) of new emerging topics. Following the detection point, the intersection decides the worth of a new topic. The algorithms presented in this paper can be used to decide the novelty and life span of an emerging topic in a specific field. The entire comprehensive collection of the ACM Digital Library is examined in the experiments. The application of the NI and PVI gives a promising indication of emerging topics in conferences and journals.

论文关键词:Topic detection and tracking,Text mining,Information retrieval,Novelty index,Published volume index,Aging theory

论文评审过程:Received 7 May 2009, Revised 2 March 2011, Accepted 13 July 2011, Available online 31 August 2011.

论文官网地址:https://doi.org/10.1016/j.ipm.2011.07.006