Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text

TL;DR

This paper introduces a new annotated corpus of Tamil-English code-mixed social media comments for sentiment analysis, addressing data scarcity and providing a benchmark for future research.

Contribution

The paper presents the creation and annotation of a large Tamil-English code-mixed sentiment corpus and evaluates baseline sentiment analysis models on it.

Findings

High inter-annotator agreement achieved

Baseline sentiment analysis results provided

Corpus serves as a benchmark for future work

Abstract

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.