Conference paper Open Access

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


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <controlfield tag="005">20201222122717.0</controlfield>
  <controlfield tag="001">4320713</controlfield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="a">Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC 2020)</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">ULTRA Arts and Science College</subfield>
    <subfield code="a">Ruba Priyadharshini</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Cardiff University</subfield>
    <subfield code="a">Vigneshwaran Muralidaran</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">National University of Ireland Galway</subfield>
    <subfield code="a">Shardul Suryawanshi</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Indian Institute of Information Technology and Management-Kerala</subfield>
    <subfield code="a">Navya Jose</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Indian Institute of Information Technology and Management-Kerala</subfield>
    <subfield code="a">Elizabeth Sherly</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">National University of Ireland Galway</subfield>
    <subfield code="0">(orcid)0000-0002-7227-1331</subfield>
    <subfield code="a">John P. McCrae</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">505797</subfield>
    <subfield code="z">md5:87ebe742a92e67576694899fc416351f</subfield>
    <subfield code="u">https://zenodo.org/record/4320713/files/chakravarthi2020overview.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-12-12</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="p">user-pret-a-llod</subfield>
    <subfield code="o">oai:zenodo.org:4320713</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">National University of Ireland Galway</subfield>
    <subfield code="a">Bharathi Raja Chakravarth</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-pret-a-llod</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">825182</subfield>
    <subfield code="a">Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;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.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.4320712</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.4320713</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">conferencepaper</subfield>
  </datafield>
</record>
22
50
views
downloads
All versions This version
Views 2222
Downloads 5050
Data volume 25.3 MB25.3 MB
Unique views 2020
Unique downloads 4848

Share

Cite as