Abstract:
This study provides complete sentiment analysis of Bengali social media posts made in
the course of the country's recent flood disaster. We use deep learning models, such as
CNN and Bi-LSTM, to evaluate a dataset of 4036 entries that have two attributes. Three
groups of attitudes are identified by the target attribute: Fear,Neutral, and Religious. Our
research uses advanced sentiment analysis tools to get an insight of the affected
community's emotional reactions. Deep learning models like Bi-LSTM and CNN in
particular are used to extract hidden expressions from the Bangla comments. The CNN
model stands out as the most successful with a brilliant accuracy of 97.91%. This
describes the model's strong capacity to identify emotions during a natural disaster and
highlights its advantage over Bi-LSTM. The results provide insightful information about
the range of emotional facets of the community's post-flood online discourse. The CNN
model's success highlights the need for customized deep learning techniques for
sentiment analysis within the particular context of social media information connected to
disasters. This study adds to our understanding of how people feel during times of crisis
and indicates how well deep learning models—in particular, CNN—work at identifying
patterns in the Bangla social media comments made during the recent flood disaster in
Bangladesh.
