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

  • Authors:

  • Yue Dai;Tuomo Kakkonen;Ernest Arendarenko;Ding Liao;Erkki Sutinen

  • Affiliations:

  • 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

  • Venue:
  • International Journal of Advanced Media and Communication
  • Year:
  • 2013

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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.