Predicting Depression from Social Networking Data using Machine Learning Techniques
3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021
; : 128-132, 2021.
Article
in English
| Scopus | ID: covidwho-1774596
ABSTRACT
In this era of COVID-19 pandemic, as more people self-isolate themselves, psychological health issues like depression, anxiety, and stress is an increasing concern all over the world. The purpose of this study is to investigate the data from social forums, where we found communities of depressed people sharing their thoughts and emotions in the forums, these forums also receive advices and support. In this paper, we will analyse the "depressed"text;by manipulating the data, extracting features, categorising, and try to understand what are the attributes of "depressed"text, and how we can "predict"whether a text should be marked as depressed or not. Using text analysis and text data mining techniques, the text obtained from the social forums was analysed and three different machine learning algorithms were used to predict depression. After cross validation overall accuracy of 99.69% was obtained as the best score using the proposed system. This study definitively answers the question regarding using human basic language and communication of personal experiences, for the prediction of depression and can be reached easily. Furthermore, not only actions, habits and behaviour of a person, text too can be used for accurate diagnosis of depression. © 2021 IEEE.
artificial intelligence; Machine learning; NLP; social forums; text mining; Data mining; Forecasting; Learning algorithms; Social networking (online); Data-mining techniques; Extracting features; Health issues; Machine learning algorithms; Machine learning techniques; Psychological health; Social forum; Social-networking; Text data mining; Text-mining
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021
Year:
2021
Document Type:
Article
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