<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="p-s-etal-2023-flamingos">
    <titleInfo>
        <title>Flamingos_python@LT-EDI-2023: An Ensemble Model to Detect Severity of Depression</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Abirami</namePart>
        <namePart type="family">P S</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Amritha</namePart>
        <namePart type="family">S</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pavithra</namePart>
        <namePart type="family">Meganathan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jerin</namePart>
        <namePart type="given">Mahibha</namePart>
        <namePart type="family">C</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Bharathi</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Chakravarthi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">B</namePart>
            <namePart type="family">Bharathi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joephine</namePart>
            <namePart type="family">Griffith</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Paul</namePart>
            <namePart type="family">Buitelaar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The prevalence of depression is increasing globally, and there is a need for effective screening and detection tools. Social media platforms offer a rich source of data for mental health research. The paper aims to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. The task is to create a system that, given social media posts in English, should classify the level of depression as ‘not depressed’, ‘moderately depressed’ or ‘severely depressed’. The paper presents the solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI@RANLP 2023. The proposed system aims to develop a machine learning model using machine learning algorithms like SVM, Random forest and Naive Bayes to detect signs of depression from social media text. The model is trained on a dataset of social media posts to detect the level of depression of the individuals as ‘not depressed’, ‘moderately depressed’ or ‘severely depressed’. The dataset is pre-processed to remove duplicates and irrelevant features, and then, feature engineering techniques is used to extract meaningful features from the text data. The model is trained on these features to classify the text into the three categories. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The ensemble model is used to combine these algorithms which gives accuracy of 90.2% and the F1 score is 0.90. The results of the proposed approach could potentially aid in the early detection and prevention of depression for individuals who may be at risk.</abstract>
    <identifier type="citekey">p-s-etal-2023-flamingos</identifier>
    <location>
        <url>https://aclanthology.org/2023.ltedi-1.47/</url>
    </location>
    <part>
        <date>2023-09</date>
        <extent unit="page">
            <start>307</start>
            <end>311</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Flamingos_python@LT-EDI-2023: An Ensemble Model to Detect Severity of Depression
%A P S, Abirami
%A S, Amritha
%A Meganathan, Pavithra
%A C, Jerin Mahibha
%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 p-s-etal-2023-flamingos
%X The prevalence of depression is increasing globally, and there is a need for effective screening and detection tools. Social media platforms offer a rich source of data for mental health research. The paper aims to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. The task is to create a system that, given social media posts in English, should classify the level of depression as ‘not depressed’, ‘moderately depressed’ or ‘severely depressed’. The paper presents the solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI@RANLP 2023. The proposed system aims to develop a machine learning model using machine learning algorithms like SVM, Random forest and Naive Bayes to detect signs of depression from social media text. The model is trained on a dataset of social media posts to detect the level of depression of the individuals as ‘not depressed’, ‘moderately depressed’ or ‘severely depressed’. The dataset is pre-processed to remove duplicates and irrelevant features, and then, feature engineering techniques is used to extract meaningful features from the text data. The model is trained on these features to classify the text into the three categories. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The ensemble model is used to combine these algorithms which gives accuracy of 90.2% and the F1 score is 0.90. The results of the proposed approach could potentially aid in the early detection and prevention of depression for individuals who may be at risk.
%U https://aclanthology.org/2023.ltedi-1.47/
%P 307-311

Markdown (Informal)

[Flamingos_python@LT-EDI-2023: An Ensemble Model to Detect Severity of Depression](https://aclanthology.org/2023.ltedi-1.47/) (P S et al., LTEDI 2023)

• Flamingos_python@LT-EDI-2023: An Ensemble Model to Detect Severity of Depression (P S et al., LTEDI 2023)

ACL

• Abirami P S, Amritha S, Pavithra Meganathan, and Jerin Mahibha C. 2023. Flamingos_python@LT-EDI-2023: An Ensemble Model to Detect Severity of Depression. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 307–311, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.