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)
This paper presents a method for using semantic analysis to identify individuals at risk of suicide in social media. We propose a machine learning approach that analyses the text of social media posts to identify key indicators of suicidal behavior. Our method involves collecting social media data, pre-processing the data, labelling the data, training a machine learning model, and testing the model. We evaluate the effectiveness of our approach using metrics such as precision, recall, and F1 score. Our results show that our method is effective at identifying individuals at risk of suicide in social media, with an F1 score of 0.85.
Keywords:
sentiment analysis, social media, suicide prevention, machine learning, natural language processing
Cite Article:
"SENTIMENT ANALYSIS ON SOCIAL NETWORKING SITES TO AVOID SUICIDE USING AI", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.b479-b482, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305161.pdf
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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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