Filters








24 Hits in 4.7 sec

Mental Illness Detection at the World Well-Being Project for the CLPsych 2015 Shared Task

Daniel Preoţiuc-Pietro, Maarten Sap, H. Andrew Schwartz, Lyle Ungar
2015 Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality  
This article is a system description and report on the submission of the World Well-Being Project from the University of Pennsylvania in the 'CLPsych 2015' shared task.  ...  The goal of the shared task was to automatically determine Twitter users who self-reported having one of two mental illnesses: post traumatic stress disorder (PTSD) and depression.  ...  Discussion and Conclusions This paper reported on the participation of the World Well-Being Project in the CLPsych 2015 shared task on identifying users having PTSD or depression.  ... 
doi:10.3115/v1/w15-1205 dblp:conf/naacl/Preotiuc-Pietro15a fatcat:3e6plukdbjcgdlghfpmgt6gatm

Deep Learning for Depression Detection of Twitter Users

Ahmed Husseini Orabi, Prasadith Buddhitha, Mahmoud Husseini Orabi, Diana Inkpen
2018 Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic  
Acknowledgments This research is funded by Natural Sciences and Engineering Research Council of Canada (NSERC) and the Ontario Centres of Excellence (OCE).  ...  Our embedding models are pre-trained on the CLPsych 2015 Shared task data.  ...  Table 1 : 1 CLPSych 2015 shared task dataset statis- tics tal illnesses. Table 2 : 2 Performance of our models on the CLPsych 2015 dataset with 5-fold cross-validation.  ... 
doi:10.18653/v1/w18-0609 dblp:conf/acl-clpsych/OrabiBOI18 fatcat:ourth4vvu5aqfoajs2zasvugye

CLPsych 2015 Shared Task: Depression and PTSD on Twitter

Glen Coppersmith, Mark Dredze, Craig Harman, Kristy Hollingshead, Margaret Mitchell
2015 Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality  
This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks.  ...  The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a  ...  We used the demographic classification tool from the World Well-Being Project (Sap et al., 2014) 2 .  ... 
doi:10.3115/v1/w15-1204 dblp:conf/naacl/CoppersmithDHHM15 fatcat:itkfl4kbcfa5vmzxed7gph5ih4

Cost-sensitive Boosting Pruning Trees for depression detection on Twitter [article]

Lei Tong, Zhihua Liu, Zheheng Jiang, Feixiang Zhou, Long Chen, Jialin Lyu, Xiangrong Zhang, Qianni Zhang, Abdul Sadka Senior, Yinhai Wang, Ling Li, Huiyu Zhou
2022 arXiv   pre-print
Our approach, which is innovative to the practice of depression detection, does not rely on the extraction of numerous or complicated features to achieve accurate depression detection.  ...  Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.  ...  ACKNOWLEDGEMENTS The work of Huiyu Zhou is supported in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342.  ... 
arXiv:1906.00398v3 fatcat:cya2gpg6zrfihimij2ywjzhvue

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Akkapon Wongkoblap, Miguel A Vadillo, Vasa Curcin
2017 Journal of Medical Internet Research  
And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.  ...  If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details.  ...  This research was supported by the UK National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London.  ... 
doi:10.2196/jmir.7215 pmid:28663166 pmcid:PMC5509952 fatcat:56uzthyd35cwdgukb2shbc2w5y

Identifying Depression on Twitter [article]

Moin Nadeem
2016 arXiv   pre-print
aid in diagnosis, even possibly enabling those suffering from depression to be more proactive about recovering from their mental health.  ...  We believe that this method may be helpful in developing tools that estimate the risk of an individual being depressed, can be employed by physicians, concerned individuals, and healthcare agencies to  ...  Data In this study, we gathered information from the Shared Task organizers of the CLPsych 2015 conference.  ... 
arXiv:1607.07384v1 fatcat:bh3uhreydbgbho7djwlrfruvuq

Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks [article]

Shaoxiong Ji, Xue Li, Zi Huang, Erik Cambria
2021 arXiv   pre-print
Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention.  ...  However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity.  ...  Acknowledgments The authors would like to thank Philip Resnik for providing the UMD Reddit Suicidality Dataset and Mark Dredze for providing the dataset in the CLPsych 2015 shared task.  ... 
arXiv:2004.07601v3 fatcat:amgogcdh75hshhg6klv25lqzdu

"Because... I was told... so much": Linguistic Indicators of Mental Health Status on Twitter

Janith Weerasinghe, Kediel Morales, Rachel Greenstadt
2019 Proceedings on Privacy Enhancing Technologies  
Topics and words related to mental health are some of the top predictors. These findings have implications for early detection of mental illnesses. However, they also raise numerous privacy concerns.  ...  Our results show that machine learning can be used to make predictions even if the users do not actively talk about their mental illness.  ...  Glen Coppersmith for providing us the CLPsych dataset, and James Pennebaker for providing us the stream-of-consciousness essays dataset.  ... 
doi:10.2478/popets-2019-0063 dblp:journals/popets/WeerasingheMG19 fatcat:mqtiwry6xrhhpfsn5iomx7dcyu

Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter

Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, Jordan Boyd-Graber
2015 Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality  
In this paper, we explore the use of supervised topic models in the analysis of linguistic signal for detecting depression, providing promising results using several models.  ...  Data Our primary experimental dataset is the Twitter collection created by Coppersmith et al. (2014)  ...  Dredze, Jamie Pennebaker, and their colleagues for kindly sharing data and resources.  ... 
doi:10.3115/v1/w15-1212 dblp:conf/naacl/ResnikACNNB15 fatcat:ipiivoywlzainfxmcou7fwvwwi

An Optimized Hybrid Neural Network Model for Detecting Depression among Twitter Users

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The Neural Network is trained in such a way that at any point when presented with a Tweet the model outputs the polarity associated with the Tweet.  ...  Also, a user-friendly GUI is presented to the user that loads the trained neural network in no time and can be used to analyze the users' state of depression.  ...  That ranked 1st in the CLPsych 2015 shared task. A linear classifier was developed by Preotiuc-Pietro et al.  ... 
doi:10.35940/ijitee.j9590.0881019 fatcat:5o5j663pdfb2bhq7vsz3iced44

CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts [article]

Muskan Garg, Chandni Saxena, Veena Krishnan, Ruchi Joshi, Sriparna Saha, Vijay Mago, Bonnie J Dorr
2022 arXiv   pre-print
Research community has witnessed substantial growth in the detection of mental health issues and their associated reasons from analysis of social media.  ...  Our contributions for causal analysis are two-fold: causal interpretation and causal categorization. We introduce an annotation schema for this task of causal analysis.  ...  We also acknowledge Amrith Krishna for his kind support and for proofreading this manuscript.  ... 
arXiv:2207.04674v1 fatcat:irdg2st7bnh73ljaztubdpdqra

Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences

Marcel Trotzek, Sven Koitka, Christoph M. Friedrich
2018 IEEE Transactions on Knowledge and Data Engineering  
Furthermore, the currently popular ERDE score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated.  ...  Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.  ...  There also exist several other lexicons that have not been evaluated, for example from the World Well-Being Project at University of Pennsylvania 13 .  ... 
doi:10.1109/tkde.2018.2885515 fatcat:j453v3rur5bnvgrvos5ogslqhy

Psychological Analysis for Depression Detection from Social Networking Sites

Sonam Gupta, Lipika Goel, Arjun Singh, Ajay Prasad, Mohammad Aman Ullah, Alexander Hošovský
2022 Computational Intelligence and Neuroscience  
With the growth of the Internet, social networks (Twitter, Facebook, Telegram, and Instagram) have become popular forums for people to share their thoughts, psychological behavior, and emotions.  ...  The results show that the LSTM classification model outperforms the other baseline models in the depression detection healthcare approach for both balanced and imbalanced data.  ...  current mental state of the user. e data were gathered from the CLPsych 2015 conference in which the latest 3000 public tweets are available.  ... 
doi:10.1155/2022/4395358 pmid:35432513 pmcid:PMC9007657 fatcat:qkal6oe3pbdhxppclk4htx5lza

Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

Saif M. Mohammad
2022 Computational Linguistics  
Systems for automatic emotion recognition (AER) and sentiment analysis can be facilitators of enormous progress (e.g., in improving public health and commerce) but also enablers of great harm (e.g., for  ...  The objective of the ethics sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems.  ...  Acknowledgments I am grateful to Annika Schoene, Mallory Feldman, and Tara Small for their belief and encouragement in the early days of this project.  ... 
doi:10.1162/coli_a_00433 fatcat:474cgulf65c5rbxem5u65qdc5q

Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

Dhiaa A. Musleh, Taef A. Alkhales, Reem A. Almakki, Shahad E. Alnajim, Shaden K. Almarshad, Rana S. Alhasaniah, Sumayh S. Aljameel, Abdullah A. Almuqhim
2022 Computers Materials & Continua  
The aim of the present work was to develop a model for analyzing Arabic users' tweets and detecting depression among Arabic Twitter users.  ...  In Arabic culture, there is a lack of awareness regarding the importance of facing and curing mental health diseases.  ...  Acknowledgement: We hope that our work will benefit the community by sharing awareness of the existence of the depression in our community and expanding the Arabic sentiment analysis research scope, and  ... 
doi:10.32604/cmc.2022.022508 fatcat:trix4jdnl5e67kdvi7hye6sphm
« Previous Showing results 1 — 15 out of 24 results