Framework for Opinion Mining Approach to Augment Education System Performance

TL;DR

This paper presents a Naive Bayes-based framework for opinion mining in education, aiming to analyze social media data to identify issues and improve the education system.

Contribution

It introduces a novel application of Naive Bayes opinion mining specifically tailored for educational data analysis.

Findings

Framework effectively classifies opinions related to education.

Predictions can guide improvements in the education system.

Method demonstrates potential for real-world educational decision-making.

Abstract

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 Na\"ive Bayes approach to the education dataset. The various phases of Na\"ive Bayes approach include three steps: conversion of data into frequency table, making classes of dataset and apply the Na\"ive 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.