SENTIMENT ANALYSIS ON SOCIAL NETWORKING SITES TO AVOID SUICIDE USING AI

Registration ID: IJNRD_193406

Published ID: IJNRD2305161

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Waghule Priyanka Ajinath, Prof. Sapike Nikita S.

sentiment analysis, social media, suicide prevention, machine learning, natural language processing

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.

Published Paper   E-Certificate

Waghule Priyanka Ajinath & Prof. Sapike Nikita S. (May-2023). SENTIMENT ANALYSIS ON SOCIAL NETWORKING SITES TO AVOID SUICIDE USING AI. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), b479-b482. https://ijnrd.org/papers/IJNRD2305161.pdf

Volume 8 Issue 5, May-2023

Pages : b479-b482

Paper Reg. ID: IJNRD_193406

Published Paper Id: IJNRD2305161

Downloads: 000122254

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Beed, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305161.pdf

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