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    <abstract>Various biological, genetic, psychological or social factors that feature a target oriented life with chronic stress and frequent traumatic experiences, lead to pessimism and apathy. The massive scale of depression should be dealt with as a disease rather than a ‘phase’ that is neglected by the majority. However, not a lot of people are aware of depression and its impact. Depression is a serious issue that should be treated in the right way. Many people dealing with depression do not realize that they have it due to the lack of awareness. This paper aims to address this issue with a tool built on the blocks of machine learning. This model analyzes the public social media texts and detects the signs of depression under three labels namely “not depressed”, “moderately depressed”, and “severely depressed” with high accuracy. The ensembled model uses three learners namely Multi-Layered Perceptron, Support Vector Machine and Multinomial Naive Bayes Classifier. The distinctive feature in this model is that it uses Artificial Neural Networks, Classifiers, Regression and Voting Classifiers to compute the final result or output.</abstract>
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%0 Conference Proceedings
%T SIS@LT-EDI-2023: Detecting Signs of Depression from Social Media Text
%A B K, Sulaksha
%A S, Shruti Krishnaveni
%A Steeve, Ivana
%A B, Monica Jenefer
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F b-k-etal-2023-sis
%X Various biological, genetic, psychological or social factors that feature a target oriented life with chronic stress and frequent traumatic experiences, lead to pessimism and apathy. The massive scale of depression should be dealt with as a disease rather than a ‘phase’ that is neglected by the majority. However, not a lot of people are aware of depression and its impact. Depression is a serious issue that should be treated in the right way. Many people dealing with depression do not realize that they have it due to the lack of awareness. This paper aims to address this issue with a tool built on the blocks of machine learning. This model analyzes the public social media texts and detects the signs of depression under three labels namely “not depressed”, “moderately depressed”, and “severely depressed” with high accuracy. The ensembled model uses three learners namely Multi-Layered Perceptron, Support Vector Machine and Multinomial Naive Bayes Classifier. The distinctive feature in this model is that it uses Artificial Neural Networks, Classifiers, Regression and Voting Classifiers to compute the final result or output.
%U https://aclanthology.org/2023.ltedi-1.19/
%P 131-137

Markdown (Informal)

[SIS@LT-EDI-2023: Detecting Signs of Depression from Social Media Text](https://aclanthology.org/2023.ltedi-1.19/) (B K et al., LTEDI 2023)

• SIS@LT-EDI-2023: Detecting Signs of Depression from Social Media Text (B K et al., LTEDI 2023)

ACL

• Sulaksha B K, Shruti Krishnaveni S, Ivana Steeve, and Monica Jenefer B. 2023. SIS@LT-EDI-2023: Detecting Signs of Depression from Social Media Text. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 131–137, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.