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Publication Number

2308029

 

Page Numbers

1-5

Paper Details

Depression: OSN Users Depression Detection and Auto Motivation System using Machine Learning Techniques

Authors

V. Shanthini, K. Praveena

Abstract

Depression is viewed as the largest contributor to global disability and a major reason for suicide. Generally, clinical psychologists diagnose depressed people via face-to-face interviews following the clinical depression criteria. However, often patients tend to not consult doctors in their early stages of depression. Nowadays, people are increasingly using social media to express their moods. Sentiment Analysis (SA) is the examination of the polarity of emotions and opinions expressed in the text by using computational methods. Sentiment could be expressed implicitly or explicitly in the text. Numerous studies on mental depression have found that tweets posted by users with major depressive disorder could be utilized for depression detection. The potential of sentiment analysis for detecting depression through an analysis of social media messages has brought increasing attention to this field. In this project, we aim to predict depressed users as well as estimate their depression intensity via leveraging social media (Twitter) data, in order to aid in raising an alarm. we propose a lexicon-enhanced LSTM model. The model first uses sentiment lexicon as an extra information pre-training a word sentiment classifier and then get the sentiment embedding’s of words including the words not in the lexicon. Combining the sentiment embedding and its word embedding can make word representation more accurate. Derived from a combination of feature extraction approaches using sentiment lexicons and textual contents, these features are able to provide impressive results in terms of depression detection.

Keywords

embedded,lexicon,detection

 

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Citation

Depression: OSN Users Depression Detection and Auto Motivation System using Machine Learning Techniques. V. Shanthini, K. Praveena. 2023. IJIRCT, Volume 9, Issue 5. Pages 1-5. https://www.ijirct.org/viewPaper.php?paperId=2308029

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