MENTAL STRESS DETECTION USING TF-IDF WITH MULTINOMIAL NAIVE BAYES

Abstract

Mental stress is a major issue nowadays, especially among youngsters. The age that was considered once most carefree is now under a large amount of stress. Stress increase nowadays leads to many problems like depression, suicide, heart attack, and stroke. In this paper, we are calculating the mental stress of students one week before the exam and during the usage of the internet. Our objective is to analyze stress in the college students at different points in his life. The effect that exam pressure or recruitments stress has on the student which often goes unnoticed. We will perform an analysis on how these factors affect the mind of a student and will also correlate this stress with the time spent on the internet. In this paper, hybrid algorithm is proposed which combines Tf-Idf with Multinomial Naive Bayes and Tf-Idf with SVM. This can be reduced to an extent if such intimidating messages or comments are segregated. The process of classifying a sentence whether it is positive, negative or neutral is known as sentiment analysis. It helps in determining emotional tone behind a sentence. To classify these intimidating messages this paper proposes a Tf-Idf with Multinomial Naive Bayes and Tf-Idf with SVM approach which classifies reviews into positive or negative.