Hope Speech detection in under-resourced Kannada language

Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassur...

Full description

Saved in:
Bibliographic Details
Main Authors Hande, Adeep, Priyadharshini, Ruba, Sampath, Anbukkarasi, Thamburaj, Kingston Pal, Chandran, Prabakaran, Chakravarthi, Bharathi Raja
Format Journal Article
LanguageEnglish
Published 10.08.2021
Subjects
Computer Science - Computation and Language
Online AccessGet full text

Cover

More Information
Summary:Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in online forums. Consequently, we propose creating an English-Kannada Hope speech dataset, KanHope and comparing several experiments to benchmark the dataset. The dataset consists of 6,176 user-generated comments in code mixed Kannada scraped from YouTube and manually annotated as bearing hope speech or Not-hope speech. In addition, we introduce DC-BERT4HOPE, a dual-channel model that uses the English translation of KanHope for additional training to promote hope speech detection. The approach achieves a weighted F1-score of 0.756, bettering other models. Henceforth, KanHope aims to instigate research in Kannada while broadly promoting researchers to take a pragmatic approach towards online content that encourages, positive, and supportive.
DOI:10.48550/arxiv.2108.04616