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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: | Depression Prediction using Emotion Detection and Text Mining using Machine Learning |
| Authors Name: | Supriya Sanjay Yanare , Abhishek Bhujang Nimbalkar , Kavita Sunil Munji , Nisha Bhausaheb Pawar |
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| Author Reg. ID: |
IJNRD_189391
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| Published Paper Id: | IJNRD2303376 |
| Published In: | Volume 8 Issue 3, March-2023 |
| DOI: | |
| Abstract: | Suicide is one of the most serious social health issues that exists in today's culture. Suicidal ideation, also known as suicidal thoughts, refers to people's plans to commit suicide. It can be used as a suicide risk measure. India is among the top countries among in the world to have annual suicide rate. Social networks have been developed as a first rate factor for its users to communicate with their interested buddies and proportion their captions, photos, and videos reflecting their moods, emotions and sentiments. To increase and put in force a version which takes a facial expression images as an enter and symptoms. On the basis of that it predicts the repute of that patient whether or not he/she has been detected or now not detected for depressed. We can train version using photographs & will use it for prediction. Image captioning can be accomplished after prediction for higher visualization of report. We will also use text mining (NLP) technique to predict melancholy the usage of signs furnished with the aid of person. At final we are able to make final choice primarily based on above two techniques. To generate detailed dashboard of user disease status and to design webapp for above system. We will use CNN algorithm for speed up detection of depressed character instances and approach to become aware of high quality answers of mental health troubles. We suggest system learning method as an efficient and scalable technique. We document an implementation of the proposed method. We've evaluated the efficiency of our proposed technique the usage of a set of various psycholinguistic features. We show that our proposed method can extensively improve the accuracy and category blunders price. Key Words: Emotion Recognition, Depression, Convolutional Neural Networks, Text processing, Image processing, Sentiment analysis |
| Keywords: | Security, Reliability, Data Integrity, Block chain, health care, brain tumor. |
| Cite Article: | "Depression Prediction using Emotion Detection and Text Mining using Machine Learning ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 3, page no.d562-d566, March-2023, Available :http://www.ijnrd.org/papers/IJNRD2303376.pdf |
| Downloads: | 000118755 |
| 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:IJNRD2303376 Registration ID: 189391 Published In: Volume 8 Issue 3, March-2023 DOI (Digital Object Identifier): Page No: d562-d566 Country: Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2303376 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2303376 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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