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Stress Detection of User using Social Interaction

EasyChair Preprint no. 1948

4 pagesDate: November 13, 2019


Mental stress is becoming a threat to people’s health now a days. With the rapid pace of life, more and more people are feeling stressed. It is not easy to detect users stress in an early time to protect user [1]. We determined that students stress state is firmly diagnosed with that of his/her activities in on-line lifestyles. We initially load the data from dataset named as “Sentiment_140” from Kaggle and visualize properties from different viewpoints and afterward propose a Naïve Bayes algorithm - It is a classification technique based on Bayes; Theorem with an assumption of independence among predictors i.e. presence of a particular feature in a class is unrelated to the presence of any other feature.

Keyphrases: logistic regression, machine learning, Naive Bayes Algorithm, Random Forest, Support Vector Machine

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Rachna Agarwal and Rishabh Rathore and Satyam Yadav and Shashank Jain and Vimaljeet Singh and Yash Mishra},
  title = {Stress Detection of User using Social Interaction},
  howpublished = {EasyChair Preprint no. 1948},

  year = {EasyChair, 2019}}
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