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

TL;DR

This paper introduces MONOVAB, a new annotated corpus for Bangla multi-label emotion detection, utilizing BERT and a context-based approach to improve emotion recognition accuracy in a structurally complex language.

Contribution

The study presents the creation of the first annotated Bangla corpus for multi-label emotion detection and demonstrates the effectiveness of BERT with a context-based approach.

Findings

BERT outperformed other models in emotion detection accuracy.

The annotated corpus enables better multi-label emotion recognition in Bangla.

A web application showcases the practical use of the BERT-based model.

Abstract

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.