Published Paper Details:

Today information sharing has become an integral part of our day-to-day life. We tend to give reviews of all the things from our day to day activities to college activities and teacher reviews. Thus, by using this vast amount of data we can apply machine learning on the data and understand the sentiment behind the information shared. By understanding the sentiments properly can help in increasing the productivity of teachers and students in an academic arena. So, in our project we are going to apply sentiment analysis on the data we get from teacher and academic feedback of students. By applying sentiment analysis on the student feedback, we will improve the teaching quality thus improving the teacher quality and increase the grade of a college or institution using machine learning. To achieve this, we will de- sign a system in combination of natural language processing, machine learning and cloud computing together. We will create a mobile application where student can place reviews or teacher feedbacks. To store these feedbacks, we will use Google Cloud. Then lexicon analysis will be applied on the data using OPEN-NLP natural language processing toolkit. The data after lexicon analysis will be used to create a training dataset with two classes positive and negative. The machine learning algorithm Support vector Machine (SVM) using the self-created training dataset to get the sentiment behind the feedback. Thus, our system will put the feedback in two categories positive and negative. So, by analysing the positive and negative we can concentrate on increasing the efficiency of teachers if needed. So, by using our system a college or institution can remove the drawbacks in teaching and increase the student and teacher relationship and interaction.