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Using Topic Modelling to Correlate a Research Institution’s Outputs with Its Goals

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Advances in Information and Communication (FICC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1130))

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Abstract

With the increasing pressure on private research organizations and universities to convert research output into innovative products and services that can lead to revenue streams, it has become even more important to ensure that research performed matches research goals set. If an institution could quantitatively compare their research output with other highly successful institutions that have similar goals (e.g., their respective countries have similar needs) then this would allow them to make appropriate organizational and personnel changes. We address this problem and demonstrate the approach taken by looking at universities from countries with similar characteristics and comparing their research outputs. This is achieved by forming topic clusters using Latent Dirichlet Allocations and then using a proposed metric for comparison of abstracts with topic clusters to quantify closeness. We determine an upper bound on this metric by comparing abstracts that were used to generate the topic clusters and a lower bound by generating a dataset of randomly chosen abstracts. We also investigate trending of this comparison over time by splitting datasets based on temporal information.

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Correspondence to Nicholas Chamansingh .

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Chamansingh, N., Hosein, P. (2020). Using Topic Modelling to Correlate a Research Institution’s Outputs with Its Goals. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_13

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