MOETA: a novel text-mining model for collecting and analysing competitive intelligence
Yue Dai, Tuomo Kakkonen, Ernest Arendarenko, Ding Liao, Erkki Sutinen · International Journal of Advanced Media and Communication · 2013
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.