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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>Innovative Research Publication</PublisherName>
      <JournalTitle>International Journal of Innovative Research in Computer Science and Technology</JournalTitle>
      <EISSN>2347-5552</EISSN>
     
      <Volume-Issue>Volume 9 Issue 4</Volume-Issue>
      <PartNumber/>
      <IssueLanguage>English</IssueLanguage>
      <Season>July - August 2021</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2021</Year>
        <Month>07</Month>
        <Day>19</Day>
      </PubDate>
      <ArticleType>Computer Science and Engineering</ArticleType>
      <ArticleTitle>Detecting Depression in Tweets Using DistilBERT</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>33</FirstPage>
      <LastPage>35</LastPage>
      <AuthorList>
        <Author>
          <FirstName>U. Yasaswini</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
                      <FirstName>Y. Sasidhar</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>P. Siva Sai</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>P. Eswar</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>V. Swathi</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
           
        </Author>
      </AuthorList>
      <DOI>https://doi.org/10.21276/ijircst.2021.9.4.8</DOI>
      <Abstract>Depression is a mood disorder that will affect a person&amp;#39;s daily life. Depression can change life with all the suicidal thoughts, and the youth is straining a lot. Social media is growing tremendously day by day, and the applications like Twitter, Facebook are being used by youngsters. They share their opinions about their mood, and we can analyze a person&amp;#39;s state of mind with those tweets written on Twitter. Our paper aims to detect that the person is depressed or not by using the Tweets using distilBERT, the distilled version of BERT. This distilBERT model is used to train the data and helps to achieve higher accuracy.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Depression, Social Media, BERT, distilBERT, Tweets</Keywords>
      <URLs>
        <Abstract>https://ijircst.org/view_abstract.php?title=Detecting Depression in Tweets Using DistilBERT&amp;year=2021&amp;vol=9&amp;primary=QVJULTU5OQ==</Abstract>
      </URLs>      
    </Journal>
  </Article>
</ArticleSet>