Tekin, Murat CanTunalı, Volkan2024-07-122024-07-1220191300-70092147-588110.5505/pajes.2019.47827https://doi.org/10.5505/pajes.2019.47827https://search.trdizin.gov.tr/yayin/detay/347721https://hdl.handle.net/20.500.12415/6991In corporations, software issues and software change demands are forwarded to the Information Technology (IT) unit via a demand management system. The priority information in this system has critical importance to the IT unit. However, the priority decision that is left to the individuals who create the demand records may not always be realistic. For instance, a non-critical and low-priority demand may be created with the highest priority, and this may lead to faulty planning and eventually to customer dissatisfaction. In this work, internal customer demands were classified using text mining techniques and their priorities were predicted. The system was trained and tested with the records extracted from the demand management system of a corporation. After cleaning and preprocessing the raw textual demand data, TF-IDF (Term Frequency - Inverse Document Frequency) weighting scheme was used when creating the document-term matrix. Several classification algorithms were tested on the data set generated, and the highest performance was obtained by Sequential Minimal Optimization algorithm with 54.1% F-Score. In addition, on the dataset made balanced with oversampling technique, the highest performance was achieved by Random Forest algorithm with 74.5% F-Score.trinfo:eu-repo/semantics/openAccessSoftware EngineeringDemand PrioritizationMachine LearningText ClassificationRandom ForestArticle620561534772125WOS:000490929500013N/A