Title: MOETA: a novel text-mining model for collecting and analysing competitive intelligence

Authors: Yue Dai; Tuomo Kakkonen; Ernest Arendarenko; Ding Liao; Erkki Sutinen

Addresses: School of Computing, University of Eastern Finland, 80110, Joensuu, Finland ' School of Computing, University of Eastern Finland, 80110, Joensuu, Finland ' School of Computing, University of Eastern Finland, 80110, Joensuu, Finland ' School of Computing, University of Eastern Finland, 80110, Joensuu, Finland ' School of Computing, University of Eastern Finland, 80110, Joensuu, Finland

Abstract: The internet constitutes a vast repository of textual information, and its emergence has dramatically changed the environment in which businesses operate. Its development has had a great influence on the current business models. The goal of this work is to outline a novel text-mining-based decision-support model, Mining for Opinion, Event and Timeline Analysis (MOETA), which aims to explore competitive intelligence from the internet and the internal textual data sources of a company in depth. MOETA integrates novel Natural Language Processing (NLP) technologies for event detection and opinion mining to locate events and opinions on a timeline. The aim is to distil unstructured textual data into knowledge and intelligence that are useful to business decision-makers. An overview of the model is given and the architecture of a system based on the model is introduced. Moreover, we provide a practical example to explain how MOETA can support decision making.

Keywords: text mining; competitive intelligence; event detection; opinion mining; timeline analysis; internet; natural language processing; NLP; modelling; decision making; decision support systems; DSS.

DOI: 10.1504/IJAMC.2013.053672

International Journal of Advanced Media and Communication, 2013 Vol.5 No.1, pp.19 - 39

Published online: 02 Sep 2013 *

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