Detecting Subjectivity in Staff Perfomance Appraisals by Using Text Mining Teachers' Appraisals of Palestinian Government Case Study

The objective of this work is to propose a text mining based approach that supports Human Resources Management (HRM) in detecting subjectivity in staff performance appraisals. The approach detects three domain-driven clues of subjectivity in reviews, where each clue represents a level of subjectivity. A considerable effort has been directed to detecting subjectivity in opinion reviews. However, to the best of our knowledge, there is no previous work that detects subjectivity in staff appraisals. For proving our approach, we applied it to the teachers' appraisals of the Palestinian government. According to our experiments, we found that the approach is effective regarding our evaluations; where we used: expert opinion, precision, recall, accuracy and F-measure. In the first level, we reached the F-measure of 88%, in the second level, we used expert staff's opinion, where they decided the percentage of duplication to be 85% and in the third level, we achieved the best average F-measure of 84%.