Corpus creation for sentiment analysis in code-mixed Tamil-English text
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Date
2020-05-11Metadata
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Chakravarthi, Bharathi Raja, Muralidaran, Vigneshwaran, Priyadharshini, Ruba, & McCrae, John P. (2020). Corpus creation for sentiment analysis in code-mixed Tamil-English text. Paper presented at the Language Resources and Evaluation Conference (LREC 2020), 1st Joint Workshop of SLTU (Spoken Language Technologies for Under-resourced languages) and CCURL (Collaboration and Computing for Under-Resourced Languages) Marseille, France, 11-16 May.
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Abstract
Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis
of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos
on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they
contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a
low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English
code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of
creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on
this corpus as a benchmark
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