Abstract:
Emotion classification holds significant
importance in various domains. However, the development of
accurate emotion classification models for the Bengali language
has been relatively limited, despite its vast speaker base. The
unique characteristics of Bengali present several challenges for
emotion classification. Consequently, there is an urgent demand
for robust and contextually-aware emotion classification models
tailored to the linguistic nuances of Bengali. This paper presents
a comprehensive study on emotion classification in Bengali text,
aiming to develop robust and effective models specific to the
language. We explored a range of ML and DL models, including
LR, SVC, CNN, LSTM, and BiLSTM. Additionally, we
proposed novel hybrid architectures, combining CNN with
LSTM and CNN with BiLSTM, to leverage both local and
contextual information from Bengali text. However, the lack of
comprehensive Bengali emotion datasets further hinders the
development of dedicated emotion classification models for the
language. To facilitate research, we created the ’Bengali
Emotion Dataset’ consisting of 14,334 social media comments,
accurately labeled into seven emotion classes. The results
demonstrate that the hybrid models, particularly
CNN+BiLSTM, outperform individual ML and DL models,
achieving the highest accuracy and F1 score of 88.45% and
88.42% respectively. The benchmark dataset and the success of
the hybrid model pave the way for more empathetic and
contextually-aware natural language processing applications
for Bengali speakers.
