Sentiment analysis is the task of identifying subjective opinions or responses about a given topic. It has been an active area of research in the past two decades in both academia and industry. There is an increasing demand for sentiment analysis on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community where the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text. This shared task presents a new gold standard corpus for sentiment analysis of code-mixed text in Dravidian languages (Malayalam-English and Tamil-English).
Malayalam is one of the Dravidian languages spoken in the southern region of India with official recognition in the Indian state of Kerala and the Union Territories of Lakshadweep and Puducherry. There are nearly 38 million Malayalam speakers in India and other countries. Malayalam is a deeply agglutinative language. Tamil is a Dravidian language spoken by Tamil people in India, Sri Lanka and by the Tamil diaspora around the world, with official recognition in India, Sri Lanka and Singapore. The Malayalam script is the Vatteluttu alphabet extended with symbols from the Grantha alphabet. The Tamil script evolved from the Brahmi script, Vatteluttu alphabet, and Chola-Pallava script. It has 12 vowels, 18 consonants, and 1 āytam (voiceless velar fricative). Minority languages such as Saurashtra, Badaga, Irula, and Paniya are also written in the Tamil script. Both Tamil and Malayalam scripts are alpha-syllabic, belonging to a family of the abugida writing system that is partially alphabetic and partially syllable-based. However, social media users often adopt Roman script for typing because it is easy to input. Hence, the majority of the data available in social media for these under-resourced languages are code-mixed.
The goal of this task is to identify sentiment polarity of the code-mixed dataset of comments/posts in Dravidian Languages (Malayalam-English and Tamil-English) collected from social media. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated with sentiment polarity at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. Our proposal aims to encourage research that will reveal how sentiment is expressed in code-mixed scenarios on social media.
The participants will be provided development, training and test dataset.
This is a message-level polarity classification task. Given a Youtube comment, systems have to classify the comment into positive, negative, neutral, mixed emotions or if the comment is not in the intended language.
As far as we know, this is the first shared task on Sentiment Analysis in Dravidian Code-Mixed text. It is co-located with 12th meeting of Forum for Information Retrieval Evaluation 2020 which will be held virtually in Hyderabad, India.