Abstract :
Teacher and course evaluation by students at the end of each term is an important task in almost every academic institution worldwide. It helps in assessing faculty performance and suitability of the course in any academic program. The data collected from evaluation comprises of two parts, Likert Scale and open-ended feedback. Computationally, the Likert Scale form can be handled easily as it is numerical in nature but to handle open-ended feedback is a challenging task. Presently, in most of the organizations it is processed manually, which contains many problems like it is error-prone, tedious and full of human biases. The objective of this study is to solve these problems, using two-step rule-based strategy from Machine Learning and Natural Language Processing (NLP) techniques. The first step is to extract overall topic of the feedback text using supervised machine learning followed by exploitation of NLP rules to find out specific aspect and related opinion word about which the feedback is given along with orientation of the opinion either positive, negative or neutral. Using, this two-step strategy combining with NLP, machinelearning techniques and data from past seven years of real feedback at a public sector university in Pakistan, we are able toachieve a recall and precision of 83.89% and 84% on topic identification i.e. to classify a feedback in teacher and course category. The system is able to extract different aspects of teacher and course with a precision of 83% and recall of 80%, whereas overall sentiment classification accuracy is 90%. To the best of our knowledge, this is the first rule-based approach for such problem with quite satisfactory results.
Keyword :
Opinion Mining, NaturalLanguage Processing (NLP), Text Mining \Teacher's / Course Evaluation.