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

Issue: September 2023

Volume 8 | Issue 9

Impact factor: 8.15

Click Here For more Info

Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter

Copyright Infringement Claims
Indexing Partner
Published Paper Details
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: 000251439
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

Click Here to Download This Article

Article Preview

Click here for Article Preview

Major Indexing from www.ijsdr.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

Track Paper
Important Links
Conference Proposal
DOI (A digital object identifier)

Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Creative Commons License
This material is Open Knowledge
This material is Open Data
This material is Open Content
Social Media

Indexing Partner