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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Stress Detection Using Natural Language Processing And Machine Learning |
| Authors Name: | Jerripotula Priyanka , Prof. K. Venkata Rao |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_205327
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| Published Paper Id: | IJNRD2309181 |
| Published In: | Volume 8 Issue 9, September-2023 |
| DOI: | |
| Abstract: | Nowadays many users posts tweets based on their mental condition about the things that happen in their day to day lives on the social media platforms. It is very important to detect and manage stress before it goes into a severe problem. A huge number of informal messages are posted every day in social networking sites, blogs and discussion forums. This paper describes an approach to detect the stress using the information from social media networking sites, like twitter. This project performs the operations involving data collection, data cleaning, training the machine and predicting the stressed and non-stressed users. This will be using the Natural Language Processing (NLP) and Machine Learning algorithms which include KNN ,Naïve bayes BernoulliNB, Random Forest, Decision tree and SVM. Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, we find that users stress state is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically study the correlation of users stress states and social interactions. We first define a set of stress-related textual undergoes the training of the machine followed by the machine learning algorithms for better results. Thus the proposed system takes the tweets as input and decides whether it is stressed or non-stressed. |
| Keywords: | NLP, KNN, SVM, Navie Bayes, Random Forest |
| Cite Article: | "Stress Detection Using Natural Language Processing And Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 9, page no.b720-b727, September-2023, Available :http://www.ijnrd.org/papers/IJNRD2309181.pdf |
| Downloads: | 000118764 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2309181 Registration ID: 205327 Published In: Volume 8 Issue 9, September-2023 DOI (Digital Object Identifier): Page No: b720-b727 Country: Visakhapatnam, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2309181 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2309181 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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