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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Detection of Depression from Twitter Activity |
| Authors Name: | Sangeeta R. Kamite , Prof. V.B. Kamble |
| Unique Id: | IJSDR2006083 |
| Published In: | Volume 5 Issue 6, June-2020 |
| Abstract: | In this paper we tend to estimate the degree of depression by exploitation social networks activities of users. By exploitation social networks users communicate with their friends and share their life activities like concepts, photos, and videos reflective their moods, feelings and sentiments. It’s doable to research the social network knowledge which has user’s feelings and sentiments to envision their moods and social network behavior once they square measure communication via numerous on-line social networking tools. ways though detection of depression victimisation social networks facts has taken a old perform internationally, there square measure numerous degrees which could be to be detected during this study, we have a tendency to purpose to guage depression analysis on social networking tool l knowledge collected from a web public supply like twitter. To standardize the consequence of depression detection, we have a tendency to used machine learning technique associate degreed algorithmic rule as an competent and scalable technique. we have a tendency to implement the planned system victimisation machine learning algorithmic rule. we've got evaluated the potency of our planned technique employing a set of varied machine learning algorithmic rule like random forest algorithmic rule and naive byes algorithmic rule. we have a tendency to show that our planned technique will considerably improve the accuracy and classification error rate. additionally, the result shows the best accuracy than different Machine learning approaches to search out period. Machine learning techniques determine top quality solutions of mental state issues among twitter users. |
| Keywords: | Social media; Depression; Twitter; Machine learning |
| Cite Article: | "Detection of Depression from Twitter Activity", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 6, page no.485 - 487, June-2020, Available :http://www.ijsdr.org/papers/IJSDR2006083.pdf |
| Downloads: | 000346976 |
| Publication Details: | Published Paper ID: IJSDR2006083 Registration ID:191970 Published In: Volume 5 Issue 6, June-2020 DOI (Digital Object Identifier): Page No: 485 - 487 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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