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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