A TEXT MINING TECHNIQUE FOR MANUFACTURING SUPPLIER CLASSIFICATION

The web presence of manufacturing suppliers is constantly increasing and so does the volume of textual data available online that pertains to the capabilities of manufacturing suppliers. To process this large volume of data and infer new knowledge about the capabilities of manufacturing suppliers, different text mining techniques such as association rule generation, classification, and clustering can be applied. This paper focuses on classification of manufacturing suppliers based on the textual description of their capabilities available in their online profiles. A probabilistic technique that adopts Naive Bayes method is adopted and implemented using R programming language. Casting and CNC machining are used as the examples classes of suppliers in this work. The performance of the proposed classifier is evaluated experimentally based on the standard metrics such as precision, recall, and F-measure. It was observed that in order to improve the precision of the classification process, a larger training dataset with more relevant terms must be used.