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Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text

Bharathi Raja Chakravarth; Ruba Priyadharshini; Vigneshwaran Muralidaran; Shardul Suryawanshi; Navya Jose; Elizabeth Sherly; John P. McCrae


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  <dc:creator>Bharathi Raja Chakravarth</dc:creator>
  <dc:creator>Ruba Priyadharshini</dc:creator>
  <dc:creator>Vigneshwaran Muralidaran</dc:creator>
  <dc:creator>Shardul Suryawanshi</dc:creator>
  <dc:creator>Navya Jose</dc:creator>
  <dc:creator>Elizabeth Sherly</dc:creator>
  <dc:creator>John P. McCrae</dc:creator>
  <dc:date>2020-12-12</dc:date>
  <dc:description>Sentiment analysis of Dravidian languages has received attention in recent years. However, most social media text is code-mixed and there is no research available on sentiment analysis of code-mixed Dravidian languages. The Dravidian-CodeMix-FIRE 2020, a track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text, focused on creating a platform for researchers to come together and investigate the problem. There were two languages for this track: (i) Tamil, and (ii) Malayalam. The participants were given a dataset of YouTube comments and the goal of the shared task submissions was to recognise the sentiment of each comment by classifying them into positive, negative, neutral, mixed-feeling classes or by recognising whether the comment is not in the intended language. The performance of the systems was evaluated by weighted-F1 score.</dc:description>
  <dc:identifier>https://zenodo.org/record/4320713</dc:identifier>
  <dc:identifier>10.5281/zenodo.4320713</dc:identifier>
  <dc:identifier>oai:zenodo.org:4320713</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/825182/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.4320712</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/pret-a-llod</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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