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


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.4320713", 
  "language": "eng", 
  "title": "Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text", 
  "issued": {
    "date-parts": [
      [
        2020, 
        12, 
        12
      ]
    ]
  }, 
  "abstract": "<p>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.</p>", 
  "author": [
    {
      "family": "Bharathi Raja Chakravarth"
    }, 
    {
      "family": "Ruba Priyadharshini"
    }, 
    {
      "family": "Vigneshwaran Muralidaran"
    }, 
    {
      "family": "Shardul Suryawanshi"
    }, 
    {
      "family": "Navya Jose"
    }, 
    {
      "family": "Elizabeth Sherly"
    }, 
    {
      "family": "John P. McCrae"
    }
  ], 
  "id": "4320713", 
  "type": "paper-conference", 
  "event": "Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC 2020)"
}
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