MADTRAS: Dataset for aspect-based sentiment analysis of movie reviews in Tamil

Title

MADTRAS: Dataset for aspect-based sentiment analysis of movie reviews in Tamil

Creator

Arunmozhi, M.; Sunitha, R.; Rajalakshmi, D.; Mohan E, Syam

Description

The rise of online platforms has led to a growing trend of people expressing their thoughts and emotions in their native languages. Movies have been a predominant topic of discussion on online platforms where people reflect on various aspects of movies. Aspect-based Sentiment Analysis (ABSA), a computational technique, assists in examining the sentiments hidden in these discussions. Two challenges arise when attempting to use ABSA to identify sentiments in movie reviews written in the Indian regional language Tamil; the former being the unavailability of potential Tamil movie review datasets and the latter being the difficulty that arises due to the agglutinative nature of Tamil Language. This work addresses the first challenge by curating an annotated movie review dataset in Tamil, MADTRAS (Dataset for Aspect-based Sentiment Analysis of Movie Reviews in Tamil). The quality of the dataset is ensured through content and annotation evaluation. To prove the efficiency of the dataset, the multilingual BERT (mBERT) was used, and the performance was compared with other Deep Learning(DL) models. 2025 The Authors

Source

Data in Brief;Volume;63;Issue;;Article No.;112073;

Date

01-01-2025

Publisher

Elsevier Inc.

Subject

ABSA; Correlation; Data curation; mBERT; Transformers

Coverage

Arunmozhi M., Department of Computer Science, Pondicherry University, Puducherry, Kalapet, 605014, India; Sunitha R., Department of Computer Science, Pondicherry University, Puducherry, Kalapet, 605014, India; Rajalakshmi D., Department of Computer Science, Pondicherry University, Puducherry, Kalapet, 605014, India; Mohan E S., Department of Computer Science, Pondicherry University, Puducherry, Kalapet, 605014, India, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 23523409;

Format

online

Language

English

Type

Data paper

Identifier

https://doi.org/10.1016/j.dib.2025.112073

https://www.scopus.com/pages/publications/105017782327?origin=resultslist