{"title":"Detecting Subjectivity in Staff Perfomance Appraisals by Using Text Mining: Teachers Appraisals of Palestinian Government Case Study","authors":"Amani A. Abed, A. El-Halees","doi":"10.1109/PICICT.2017.25","DOIUrl":"https://doi.org/10.1109/PICICT.2017.25","url":null,"abstract":"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%.","PeriodicalId":259869,"journal":{"name":"2017 Palestinian International Conference on Information and Communication Technology (PICICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126967545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Arabic Opinion Mining Using Parallel Decision Trees","authors":"W. Ahmed, A. El-Halees","doi":"10.1109/PICICT.2017.28","DOIUrl":"https://doi.org/10.1109/PICICT.2017.28","url":null,"abstract":"Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naïve Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to certain class. Therefore, in our work, we investigate opinion mining of Arabic text at the document level, by applying decision trees classification classifier to have clear, understandable rule, also we apply parallel decision trees classifiers to have efficient results. We applied parallel decision trees on two Arabic corpus of text documents by using parallel implementation of RapidMiner tools. In case of applying parallel decision tree family on OCA we get the best results of accuracy (93.83%), f-measure (93.22) and consumed time 42 Sec at thread 4, one of the resulted rule is Urdu language lines. In case of applying parallel decision tree family on BHA we get the best results of accuracy (90.63%), f-measure (82.29) and consumed time 219 Sec at thread 4, one of the resulted rule is Urdu language lines.","PeriodicalId":259869,"journal":{"name":"2017 Palestinian International Conference on Information and Communication Technology (PICICT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126717975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}