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