Data mining approach for automatic discovering success factors relationship statements in full text articles

Abstract

In the context of Business-to-Business (B2B), an understanding of inter-organizational success factors and their impacts is crucial for effective strategic management. Several studies regarding those success factors and their influences have been conducted and published as articles. We aim at applying existing techniques, especially data mining, to automatically classify relevant sentences describing an influencing relationship between success factors. This paper presents the experiment method and results to find the optimal data mining workflow for our classification task. In particular, we apply several well-known data mining techniques based on different control factors. Then all discovered models are evaluated and compared to find the optimal data mining workflow. The main contributions include (i) the application of data mining for discovering success factors and their relationships, and (ii) the optimal workflow as a standardized flow for further similar classification tasks. The major challenge of this work is that there exists no mature corpus in this context, and hence our approach is implemented without a supporting corpus. The result shows that the models derived from the workflows that consider a section where a sentence is located perform better than the others in term of average performance. Furthermore, we found that the Support Vector Machine (SVM) performs better than other classifiers.