MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection
Sumit Kumar Banshal Sajal Das Shumaiya Akter Shammi Narayan Ranjan Chakraborty
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.