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A Survey: Stress Detection Techniques
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Author(s): Reshma Radheshamjee Baheti (MTech Student, Department of CSE, MIT Aurangabad, Maharashtra, India)and Supriya Kinariwala (Professor, Department of CSE, MIT Aurangabad, Maharashtra, India)
Copyright: 2020
Volume: 11
Issue: 1
Pages: 6
Source title:
International Journal of Applied Evolutionary Computation (IJAEC)
Editor(s)-in-Chief: Wei-Chiang Samuelson Hong (Asia Eastern University of Science and Technology, Taiwan)and Sukhpal Singh Gill (Queen Mary University of London, UK)
DOI: 10.4018/IJAEC.2020010102
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Abstract
Recently, human stress is rapidly increasing. The school-college students, job professionals, and many people those work under pressure. In last few decades, research is going on how to predict people under pressure or feeling relax with his/her duty. In survey it is evaluated, sentiment analysis will work to find emotions or feelings about their daily life. By analyzing social media network like Facebook, Twitter, and other networking sites where user can share personal feelings like happy, angry, stressed, relaxed, or any other emotion to express human life events or views regarding any topic. On social networking sites, a huge number of informal messages are posted every day, also blogs or discussion forums are also available. Emotions appear to be frequently vital in these texts for expressing friendship, and the presentation of social support as a part of opinions or view. In this article, a survey is done on existing techniques which are working to find sentiment analysis of textual data. In the textual data, the positive and negative sentences have to be found to check the emotions of the user. The survey also finds the natural language processing, the lexical parser, sentiment analysis, the classifier algorithm and some different kinds of Twitter datasets. It is found that 85% work completed on sentiment analysis and categorized the sentences as positive or negative.
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