India is a linguistic area with one of the longest histories of contact, influence, use, teaching and learning of English-in-diaspora in the world (Kachru and Nelson, 2006). Thus, a huge number of Indians active on the internet are able in English communication to some degree. India also enjoys huge diversity in language. Apart from Hindi, it has several regional languages that are the primary tongue of people native to the region. This is to the extent that social media including Facebook, WhatsApp, Twitter, etc. contain more than one language, and such phenomena are called code-mixing and code-switching. On the other side, the evolution of sentiments from such social media texts have also created many new opportunities for information access and language technology, but also many new challenges, making it one of the prime present-day research areas. Sentiment analysis in code-mixed data has several real-life applications in opinion mining from social media campaign to feedback analysis.

Linguistic processing of such social media dataset and its sentiment analysis is a difficult task. Till date, most of the experiments have been performed on identifying the languages (Bali et al., 2014; Das and Gamback, 2014), parts-of-speech tagging (Ghosh et al., 2016), etc. Few tasks also have been started on the sentiment analysis of code-mixed data such as Hindi-English (Joshi et al., 2016). Therefore, we believe that it is the best place to bring more research attention towards developing language technologies for identifying sentiments from Indian social media texts.

Main goal of this task is to identify the sentence level sentiment polarity of the code-mixed dataset of Indian languages pairs (Hi-En, Ben-Hi-En) collected from Twitter, Facebook, and WhatsApp. Each of the sentences is annotated with language information as well as polarity at the sentence level. The participants will be provided development, training and test dataset.