TamilSentiMix

Donated on 8/13/2023

We created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube.

Dataset Characteristics

Multivariate, Text

Subject Area

Computer Science

Associated Tasks

Classification

Feature Type

Categorical

# Instances

15744

# Features

-

Dataset Information

Additional Information

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.

Has Missing Values?

No

Introductory Paper

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

By Bharathi Raja Chakravarthi, V. Muralidaran, R. Priyadharshini, John P. McCrae. 2020

Published in Workshop on Spoken Language Technologies for Under-resourced Languages

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1 citations
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Keywords

Sentiment analysis

Creators

Bharathi Raja

bharathiraja.akr@gmail.com

National University of Ireland Galway

V. Muralidaran

R. Priyadharshini

John P.

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