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.
Task:
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. The sister taks for offensive language detection in Dravidian Code-Mixed text (https://sites.google.com/view/dravidian-codemix-fire2020/overview) . It is co-located with 12th meeting of Forum for Information Retrieval Evaluation 2020 which will be held virtually in Hyderabad, India.
Keynote Speakers:
- Dr. Elizabeth Sherly, Professor (HAG) of Indian Institute of Information Technology and Management-Kerala (IIITM-K), will give a talk about Dravidian Languages
- Dr. Monojit Choudhury, Researcher in Microsoft Research Lab India, will give a talk about Code-Mixing
Paper submission:
The paper (Sentiment Analysis for Davidian Languages in Code-Mixed Text) name format should be: TEAM_NAME@Dravidian-CodeMix-FIRE2020: Title of the paper. For example: NUIG_ULD@Dravidian-CodeMix-FIRE2020: Sentiment Analysis on Multilingual Code Mixing Text.
Following are some general guidelines to keep in mind while submitting the working notes.
- Basic sanity check for grammatical errors and reported results.
- Papers should have sufficient information for reproducing the mentioned results - Papers should follow the appropriate style (We will use CEUR style: details below)
- Check the papers for text reuse / plagiarism. This includes self-plagiarism as well. We would like to stress this point as CEUR is quite strict about it. Any paper found to have plagiarized content should be rejected without further consideration.
- It has been commonly observed that several teams write more than one working notes (for e.g. separate submissions for separate subtasks) and reuse a substantial portion of the text in these multiple submissions. Keeping this in mind, we will NOT be allowing multiple working notes from the same set of authors. They should be asked to merge them into one.
- Please ensure the author names do not have any salutations like Dr., Prof., etc. in the final version.
- Each paper should have a copyright clause included in the paper (See the"Author agreement variants" at http://ceur-ws.org/HOWTOSUBMIT.html)
- Each author should also submit a copyright agreement signed by the authors. (Partially filled agreement will be shared shortly).
All submissions should be in Single column CEUR format. Authors should use one of the CEUR Templates below:
- Overleaf: https://www.overleaf.com/read/gwhxnqcghhdt
- Word and Latex: http://ceur-ws.org/Vol-XXX/CEURART.zip
In general, apart from plagiarism related concerns, we would not be rejecting any paper.