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
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.
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- Publication date:
- December 12, 2020
- DOI:
-
- Grants:
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European Commission:
- Pret-a-LLOD - Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors (825182)
- Meeting:
- Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC 2020)
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International