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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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DravidianCodeMix: sentiment analysis and offensive language identification dataset for Dravidian languages in

Bharathi Raja Chakravarthi¹, Ruba Priyadharshini², Vigneshwaran Muralidaran³

¹Insight SFI Research Centre for Data Analytics, Data Science Institute, National University of Ireland Galway, Galway, Ireland.

Language Resources and Evaluation

|August 23, 2022

Summary

Researchers created a new dataset of over 60,000 social media comments in Tamil, Kannada, and Malayalam for sentiment analysis and offensive language identification. This resource aids natural language processing for under-resourced Dravidian languages.

Keywords:

Code-mixed Corpora Dravidian languages Kannada Malayalam Offensive language identification Sentiment analysis Tamil

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Area of Science:

Computational Linguistics
Natural Language Processing
Social Media Analysis

Background:

Under-resourced Dravidian languages present significant challenges for NLP tasks.
Social media platforms are rich sources of multilingual user-generated content, often exhibiting code-mixing.
Existing datasets often lack sufficient coverage for low-resource languages and specific annotation tasks.

Purpose of the Study:

To develop and release a novel, manually annotated dataset for sentiment analysis and offensive language identification.
To support research in natural language processing for Tamil, Kannada, and Malayalam.
To capture code-mixing phenomena prevalent in multilingual social media data.

Main Methods:

Manual annotation of over 60,000 YouTube comments by volunteer annotators.
Annotation focused on sentiment analysis and offensive language identification.
Dataset creation involved collecting and processing comments in Tamil-English, Kannada-English, and Malayalam-English.

Main Results:

A comprehensive multilingual dataset comprising 44,000 Tamil-English, 7,000 Kannada-English, and 20,000 Malayalam-English comments.
High inter-annotator agreement (Krippendorff's alpha) demonstrating annotation quality.
Baseline machine learning and deep learning experiments established performance benchmarks.

Conclusions:

The released dataset provides a valuable resource for advancing NLP research in under-resourced Dravidian languages.
The dataset's inclusion of code-mixing phenomena offers unique opportunities for studying language interaction.
Availability on GitHub and Zenodo promotes accessibility and further research.