Abstract
Machines can read, comprehend, and extrapolate meaning from human languages, thanks to natural language processing.In this paper, we have detected emotion from multilingual text and multi-emotional sentences.For our research, we have collected a dataset containing around 7000 tweets on 4 emotions (Anger, Fear, Joy, and Sadness). After pre-processing our data, we used 2 NLP feature extraction models and trained those with the help of 4 different Machine Learning classifiers. We have also developed an algorithm for detecting exact emotions from multi-emotional sentences. Also, we compared our result with a research paper using the same dataset (ISEAR). And found out our model provides relatively better resultsthan that model. We also tried to determine emotion from the Bangla text. Although there is not much data regarding emotion in Bengali. We managed to get around 600 data on Bangla.
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