Aspect based sentiment analysis of students opinion using machine learning techniques

Recent times, customer wants to share good and bad opinions about their experience like usage of recently purchased product, services provided by a hospital, education and so on over social media, micro blogs, review sites and etc. Today smart phones become mandatory for most of the college students. They share their experience and feelings immediately over internet applications with others. Student opinions can be collected through the internet applications and can be categorized based on various entities. We propose a new method of analyzing online student feedback collected from twitter API by measuring semantic relatedness between aspect word and student opinion sentence. The results of this work will help the students to improve their studies and helps the instructors to improve their teaching skills. In this work classification and clustering techniques have been used to categorize the opinions.