Framework for Opinion Mining Approach to Augment Education System Performance
This addresses the problem of enhancing education system performance for stakeholders like educators and policymakers, but it is incremental as it applies an existing method to a new domain.
The paper tackles the problem of malpractices in the education system by designing a framework that applies Naïve Bayes to an education dataset for opinion mining, resulting in predictions used to make improvements and provide better education.
The extensive expansion growth of social networking sites allows the people to share their views and experiences freely with their peers on internet. Due to this, huge amount of data is generated on everyday basis which can be used for the opinion mining to extract the views of people in a particular field. Opinion mining finds its applications in many areas such as Tourism, Politics, education and entertainment, etc. It has not been extensively implemented in area of education system. This paper discusses the malpractices in the present examination system. In the present scenario, Opinion mining is vastly used for decision making. The authors of this paper have designed a framework by applying Naïve Bayes approach to the education dataset. The various phases of Naïve Bayes approach include three steps: conversion of data into frequency table, making classes of dataset and apply the Naïve Bayes algorithm equation to calculate the probabilities of classes. Finally the highest probability class is the outcome of this prediction. These predictions are used to make improvements in the education system and help to provide better education.