scholarly journals Mining Twitter Data for Signs of Depression in Brazil

2019 ◽  
Author(s):  
Otto Von Sperling ◽  
Marcelo Ladeira
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
English Speakers ◽  
Learning Models ◽  
Stepping Stones ◽  
Linguistic Style ◽  
Twitter Data ◽  
Disor Ders ◽  
Large Corpus ◽  
Machine Learning Models

The literature on computerized models that help detect, study and understand signs of mental health disor- ders from social media has been thriving since the mid-2000s for English speakers. In Brazil, this area of research shows promising results, in addition to a variety of niches that still need exploring. Thus, we construct a large corpus from 2941 users (1486 depressive, 1455 non-depressive), and induce machine learning models to identify signs of depression from our Twitter corpus. In order to achieve our goal, we extract features by measuring linguistic style, behavioral patterns, and affect from users’ public tweets and metadata. Resulting models successfully distinguish between depressive and non-depressive classes with performance scores comparable to results in the literature. We hope that our findings can become stepping stones towards more methodologies being applied at the service of mental health.

Decision Level Fusion using Ensemble Classifier for Mental Disease Classification (Preprint)

2020 ◽  
Author(s):  
Maqsood Ahmad ◽  
Noorhaniza Wahid ◽  
Rahayu A Hamid ◽  
Saima Sadiq ◽  
Arif Mehmood ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mental Disease ◽  
Gradient Boosting ◽  
Learning Models ◽  
Decision Level ◽  
Decision Level Fusion ◽  
Class Labels ◽  
Level Fusion ◽  
Machine Learning Models

BACKGROUND Mental health signifies the emotional, social, and psychological well-being of a person. It also affects the way of thinking, feeling, and situation handling of a person. A stable mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in life. The mental health problems are rising globally and constituting a burden on health-care systems. Early diagnosis can help the professionals in the treatment that may lead to complications if they remain untreated. The machine learning models are highly prevalent for medical data analysis, disease diagnosis, and psychiatric nosology. OBJECTIVE This research addresses the challenge of detecting six major psychological disorders, namely, Anxiety, Bipolar Disorder, Conversion Disorder, Depression, Mental Retardation, and Schizophrenia. These challenges are mined by applying decision level fusion of supervised machine learning algorithms. METHODS observations that we used for training and testing the models. Furthermore, to reduce the impact of a conflicting decision, a voting scheme Shrewd Probing Prediction Model (SPPM) is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine (RF+GBM). RESULTS The proposed model generated the Term Frequency – Inverse Document Frequency (TF-IDF)-based average accuracy, precision, recall and F1 score of 67% thus outperforming other machine learning models namely, Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR) and Support Vector Machines (SVM). CONCLUSIONS This research provides an intuitive solution for mental disorder analysis among different target class labels or groups. A framework is proposed for determining the mental health problem of patients using observations of medical experts. The framework consists of an ensemble model based on RF and GBM with a novel SPPM technique, namely SPPM (RF+GBM). This proposed decision level fusion approach significantly improves the performance in terms of Accuracy, Recall, and F1-score with 67%, 66%, and 67% respectively. This framework seems suitable in the case of huge and more diverse multi-class datasets. Furthermore, three vector spaces based on TF-IDF (unigram, bi-gram, and tri-gram) are also tested on the machine learning models and the proposed model. Experiments revealed that unigram performed better on the experimental dataset. In the future, more physiological parameters such as respiratory rate, ECG, and EEG signals can be included as features to improve accuracy. Also, the proposed framework can be tested on a wide range of mental illness categories by adding more mental illness diseases in the dataset which will result in an increase of class labels.

scholarly journals What health records data are required for accurate prediction of suicidal behavior?

2019 ◽  
Vol 26 (12) ◽  
pp. 1458-1465 ◽  
Author(s):  
Gregory E Simon ◽  
Susan M Shortreed ◽  
Eric Johnson ◽  
Rebecca C Rossom ◽  
Frances L Lynch ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Suicide Attempt ◽  
Suicidal Behavior ◽  
Claims Data ◽  
Learning Models ◽  
Health Records ◽  
C Statistic ◽  
Insurance Claims Data ◽  
Machine Learning Models

Abstract Objective The study sought to evaluate how availability of different types of health records data affect the accuracy of machine learning models predicting suicidal behavior. Materials and Methods Records from 7 large health systems identified 19 061 056 outpatient visits to mental health specialty or general medical providers between 2009 and 2015. Machine learning models (logistic regression with penalized LASSO [least absolute shrinkage and selection operator] variable selection) were developed to predict suicide death (n = 1240) or probable suicide attempt (n = 24 133) in the following 90 days. Base models were used only historical insurance claims data and were then augmented with data regarding sociodemographic characteristics (race, ethnicity, and neighborhood characteristics), past patient-reported outcome questionnaires from electronic health records, and data (diagnoses and questionnaires) recorded during the visit. Results For prediction of any attempt following mental health specialty visits, a model limited to historical insurance claims data performed approximately as well (C-statistic 0.843) as a model using all available data (C-statistic 0.850). For prediction of suicide attempt following a general medical visit, addition of data recorded during the visit yielded a meaningful improvement over a model using all data up to the prior day (C-statistic 0.853 vs 0.838). Discussion Results may not generalize to setting with less comprehensive data or different patterns of care. Even the poorest-performing models were superior to brief self-report questionnaires or traditional clinical assessment. Conclusions Implementation of suicide risk prediction models in mental health specialty settings may be less technically demanding than expected. In general medical settings, however, delivery of optimal risk predictions at the point of care may require more sophisticated informatics capability.

scholarly journals Sentiment analysis of Social Media Text-Emoticon Post with Machine learning Models Contribution Title

2021 ◽  
Vol 2070 (1) ◽  
pp. 012079
Author(s):  
V Jagadishwari ◽  
A Indulekha ◽  
Kiran Raghu ◽  
P Harshini
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Data Sets ◽  
Learning Models ◽  
Twitter Data ◽  
The Social ◽  
Social Media Text ◽  
Machine Learning Models

Abstract Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.

Twitter Data Sentimental Analysis Using Multiple Classifications

2020 ◽  
Vol 17 (8) ◽  
pp. 3776-3781
Author(s):  
M. Adimoolam ◽  
Raghav Sharma ◽  
A. John ◽  
M. Suresh Kumar ◽  
K. Ashok Kumar
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Learning Models ◽  
The Past ◽  
Twitter Data ◽  
Learning Techniques ◽  
Machine Learning Models

In the past few decades human beings have knowledgeable tremendous intensification in the interaction in particular micro blogging websites and various social media as online resources. Many kinds of data have been used and classification data to group and store are challenging in this real world scenario. Various machine and Natural Language Processing (NLP) were being applied to analysis the sentiment. A major concentration of this work was on using several machine learning algorithms to perform sentimental analysis and comparing various machine learning models for the sentiment classification. This work analysed various sentimental using multiple classifications. From the evaluation of this experiment, it can be concluded that NLP and machine learning Techniques are efficient for sentimental analysis.

scholarly journals Using AI for Mental Health Analysis and Prediction in School Surveys

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
H S Adnan ◽  
A Srsic ◽  
P M Venticich ◽  
D M R Townend
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Analysis ◽  
Data Collection ◽  
Health Surveillance ◽  
Current Paper ◽  
Learning Models ◽  
Machine Learning Models

Abstract Background Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper-based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data-driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects. Key messages This model uses artificial intelligence to improve mental health surveillance and evaluation in school settings. Artificial intelligence can be applied more broadly in public health to harness the potential of predictive models.

Predicting Depression Using Social Media Posts

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Fahem Abu Bakar ◽  
◽  
Nazri Mohd Nawi ◽  
Abdulkareem A. Hezam ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Neural Network Model ◽  
Support Vector ◽  
Learning Models ◽  
The Neural Network ◽  
Machine Learning Models

The use of Social Network Sites (SNS) is on the rise these days, particularly among the younger generations. Users can communicate their interests, feelings, and everyday routines thanks to the availability of social media sites. Many studies show that properly utilizing user-generated content (UGC) can aid in determining people's mental health status. The use of the UGC could aid in the prediction of mental health, particularly depression, where it is a significant medical condition that impairs one's ability to work, learn, eat, sleep, and enjoy life. However, all information about a person's mood and negativism can be gathered from their SNS user profile. Therefore, this study utilizes SNS as a data source by using machine learning models to screen and identify users in categorizing users based on their mental health. The performance of three machine learning models is evaluated to classify the UGC: Decision Forest, Neural Network, and Support Vector Machine (SVM). The results show that the accuracy and recall result of the Neural Network model is the same as the Support Vector Machine (SVM) model, which is 78.27% and 0.042, but Neural Network performs better in the average precision value. This proves that the Neural Network model is the best model for making predictions to determine the level of depression by using social media posts.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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