Domain Oriented Aspect Detection for Student Feedback System

Opinion Mining becomes popular and seeking the information on online review or feedback system. In conventional opinion mining techniques, it can examine how people feel about the given topic such as positive or negative feeling upon the feedback comments. In current trend, the goal of sentiment analysis is to dig the aspect word that is the fine grained sentiment information on various domains. So, the proposed system aims to analyze the aspect level sentiment analysis on student feedback system. The required feedback data are collected from the University of Computer Studies, Taungoo(UCST). This system uses OpenNLP parser for POS tagging and sentiWordNet lexical resources for defining the wordScore. The Domain Specific Ontology relating to UCST is created in the preprocessing stage of this system which supports the main process Aspect Detection. Finally, the accuracy of this system is measured by precision and recall by applying the Naïve Bayes Classification Approach on the dataset of feedbacks and their opinion. This system will assist the administrator of UCST to evaluate the performance of the University.