Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017

TL;DR

This paper provides an overview of a shared task focused on sentiment analysis of code-mixed Hindi-English and Bengali-English social media data, highlighting challenges, datasets, and participant systems.

Contribution

It introduces a shared task for sentiment analysis on code-mixed Indian languages, detailing datasets, evaluation methods, and baseline and participant systems.

Findings

Baseline and participant systems developed for sentiment analysis.

Evaluation results on code-mixed datasets.

Insights into challenges of sentiment analysis in multilingual social media data.

Abstract

Sentiment analysis is essential in many real-world applications such as stance detection, review analysis, recommendation system, and so on. Sentiment analysis becomes more difficult when the data is noisy and collected from social media. India is a multilingual country; people use more than one languages to communicate within themselves. The switching in between the languages is called code-switching or code-mixing, depending upon the type of mixing. This paper presents overview of the shared task on sentiment analysis of code-mixed data pairs of Hindi-English and Bengali-English collected from the different social media platform. The paper describes the task, dataset, evaluation, baseline and participant's systems.