Source:

Bharathi Raja Chakravarthi, Vigneshwaran Muralidaran, Ruba Priyadharshini, John Philip McCrae,
National University of Ireland Galway. bharathiraja.akr '@' gmail.com

Data Set Information:

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.

[Web Link]#.X1oUNRQnbmF

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Relevant Papers:

Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text ([Web Link])

Citation Request:

@inproceedings{chakravarthi-etal-2020-corpus,
title = 'Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text',
author = 'Chakravarthi, Bharathi Raja and
Muralidaran, Vigneshwaran and
Priyadharshini, Ruba and
McCrae, John Philip',
booktitle = 'Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)',
month = may,
year = '2020',
address = 'Marseille, France',
publisher = 'European Language Resources association',
url = '[Web Link]',
pages = '202--210',
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.',
language = 'English',
ISBN = '979-10-95546-35-1',
}

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