# MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection

arXiv: https://arxiv.org/abs/2309.15670 · HTML: https://mlsanity.com/paper/2309.15670/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 90 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://mlsanity.com/paper/2101.00204/) (2021)
- [The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers](https://mlsanity.com/paper/1612.01556/) (2016)
- [Sentiment analysis in Bengali via transfer learning using multi-lingual BERT](https://mlsanity.com/paper/2012.07538/) (2020)
- [Bangla Text Classification using Transformers](https://mlsanity.com/paper/2011.04446/) (2020)
- [Emotion Classification in a Resource Constrained Language Using Transformer-based Approach](https://mlsanity.com/paper/2104.08613/) (2021)
- [SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews](https://mlsanity.com/paper/2011.09684/) (2020)
- [Sentiment Identification in Code-Mixed Social Media Text](https://mlsanity.com/paper/1707.01184/) (2017)
- [Anubhuti -- An annotated dataset for emotional analysis of Bengali short stories](https://mlsanity.com/paper/2010.03065/) (2020)
- [Analyzing Roles of Classifiers and Code-Mixed factors for Sentiment Identification](https://mlsanity.com/paper/1801.02581/) (2018)

## Built on by (most cited first, 2 of 2)

- [ANUBHUTI: A Comprehensive Corpus For Sentiment Analysis In Bangla Regional Languages](https://mlsanity.com/paper/2506.21686/) (2025)
- [BOISHOMMO: Holistic Approach for Bangla Hate Speech](https://mlsanity.com/paper/2504.08408/) (2025)

## Abstract (arXiv)

In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh most spoken language throughout the entire world. However, the language is structurally complicated, which makes this field arduous to extract emotions in an accurate manner. Several distinct approaches such as the extraction of positive and negative sentiments as well as multiclass emotions, have been implemented in this field of study. Nevertheless, the extraction of multiple sentiments is an almost untouched area in this language. Which involves identifying several feelings based on a single piece of text. Therefore, this study demonstrates a thorough method for constructing an annotated corpus based on scrapped data from Facebook to bridge the gaps in this subject area to overcome the challenges. To make this annotation more fruitful, the context-based approach has been used. Bidirectional Encoder Representations from Transformers (BERT), a well-known methodology of transformers, have been shown the best results of all methods implemented. Finally, a web application has been developed to demonstrate the performance of the pre-trained top-performer model (BERT) for multi-label ER in Bangla.