Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text
This addresses the problem of sentiment analysis for low-resourced languages like Tamil in social media applications, but it is incremental as it focuses on data creation rather than novel methods.
The researchers tackled the lack of annotated code-mixed data for sentiment analysis in Tamil-English text by creating a gold standard corpus of 15,744 YouTube comments, and they used it to benchmark sentiment analysis results.
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.