@inproceedings{kumari-etal-2023-ml-ai-iiitranchi-lt,
title = "{ML}{\&}{AI}{\_}{IIITR}anchi@{LT}-{EDI}-2023: Hybrid Model for Text Classification for Identification of Various Types of Depression",
author = "Kumari, Kirti and
Jha, Shirish Shekhar and
Dayanand, Zarikunte Kunal and
Sharma, Praneesh",
editor = "Chakravarthi, Bharathi R. and
Bharathi, B. and
Griffith, Joephine and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.34",
pages = "223--232",
abstract = "DepSign{--}LT{--}EDI@RANLP{--}2023 is a dedicated task that addresses the crucial issue of identifying indications of depression in individuals through their social media posts, which serve as a platform for expressing their emotions and sentiments. The primary objective revolves around accurately classifying the signs of depression into three distinct categories: {``}not depressed,{''} {``}moderately depressed,{''} and {``}severely depressed.{''} Our study entailed the utilization of machine learning algorithms, coupled with a diverse range of features such as sentence embeddings, TF-IDF, and Bag-of- Words. Remarkably, the adoption of hybrid models yielded promising outcomes, culminating in a $10^{th}$ rank achievement, supported by macro F1-Score of 0.408. This research underscores the effectiveness and potential of employing advanced text classification methodologies to discern and identify signs of depression within social media data. The findings hold implications for the development of mental health monitoring systems and support mechanisms, contributing to the well-being of individuals in need.",
}
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<abstract>DepSign–LT–EDI@RANLP–2023 is a dedicated task that addresses the crucial issue of identifying indications of depression in individuals through their social media posts, which serve as a platform for expressing their emotions and sentiments. The primary objective revolves around accurately classifying the signs of depression into three distinct categories: “not depressed,” “moderately depressed,” and “severely depressed.” Our study entailed the utilization of machine learning algorithms, coupled with a diverse range of features such as sentence embeddings, TF-IDF, and Bag-of- Words. Remarkably, the adoption of hybrid models yielded promising outcomes, culminating in a 10^th rank achievement, supported by macro F1-Score of 0.408. This research underscores the effectiveness and potential of employing advanced text classification methodologies to discern and identify signs of depression within social media data. The findings hold implications for the development of mental health monitoring systems and support mechanisms, contributing to the well-being of individuals in need.</abstract>
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%0 Conference Proceedings
%T ML&AI_IIITRanchi@LT-EDI-2023: Hybrid Model for Text Classification for Identification of Various Types of Depression
%A Kumari, Kirti
%A Jha, Shirish Shekhar
%A Dayanand, Zarikunte Kunal
%A Sharma, Praneesh
%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 kumari-etal-2023-ml-ai-iiitranchi-lt
%X DepSign–LT–EDI@RANLP–2023 is a dedicated task that addresses the crucial issue of identifying indications of depression in individuals through their social media posts, which serve as a platform for expressing their emotions and sentiments. The primary objective revolves around accurately classifying the signs of depression into three distinct categories: “not depressed,” “moderately depressed,” and “severely depressed.” Our study entailed the utilization of machine learning algorithms, coupled with a diverse range of features such as sentence embeddings, TF-IDF, and Bag-of- Words. Remarkably, the adoption of hybrid models yielded promising outcomes, culminating in a 10^th rank achievement, supported by macro F1-Score of 0.408. This research underscores the effectiveness and potential of employing advanced text classification methodologies to discern and identify signs of depression within social media data. The findings hold implications for the development of mental health monitoring systems and support mechanisms, contributing to the well-being of individuals in need.
%U https://aclanthology.org/2023.ltedi-1.34
%P 223-232
Markdown (Informal)
[ML&AI_IIITRanchi@LT-EDI-2023: Hybrid Model for Text Classification for Identification of Various Types of Depression](https://aclanthology.org/2023.ltedi-1.34) (Kumari et al., LTEDI-WS 2023)
ACL