Assessing American presidential candidates using principles of ontological engineering, word sense disambiguation, data envelope analysis and qualitative comparative analysis

Word sense disambiguation (WSD) is the process of automatically identifying which the appropriate meaning of a word given in its sentence. WSD is a promising research area in computational linguistics, especially in wide range of advanced applications, such as medical and social sciences. This research employs the concept (WSD) to determine the inherent meaning of voter intentions regarding possible political candidates. Where candidates can be examined and their true assets and competencies in three major areas of eligibility, education, and experience inputs can be deciphered. Data envelope analysis (DEA) is used to determine underlying word instances for elected and successful outputs. The results demonstrate the validity of using (DEA) as a tool for (WSD). The results also indicate that the survey administered by the website which is developed for the purpose of this research, and used in this study, is a promising tool for predicting successful presidential candidates. We further validated our research findings by employing a qualitative comparative analysis approach to define the fuzzy relationships found in our data.