Bengali & Banglish: A Monolingual Dataset for Emotion Detection in Linguistically Diverse Contexts

Published: 25 April 2024| Version 1 | DOI: 10.17632/4dnrwbxt8n.1
Moshiur Rahman Faisal, Ashrin Mobashira Shifa Ashrin Mobashira Shifa,
, Rashedur M Rahman


This dataset, positioned at the intersection of Bengali and Banglish (an English-character variant of Bengali), is a valuable resource for emotion detection. It encompasses a total of 80,098 data entries, comprising both languages. The dataset is organized into six distinct emotional categories: anger (15,179), disgust (13,098), fear (7,565), joy (17,836), sadness (16,309), and surprise (10,107), aligning with Ekman's six basic emotions framework. Sourced from platforms such as EmoNoBa, UBMEC, MONOVAB, and comments from YouTube and Twitter posts, it offers a diverse and rich dataset for research and analysis. Moreover, given its bilingual nature, this data also holds relevance for neural machine translation tasks.



North South University


Natural Language Processing, Machine Translation, Bengali Language, Emotion