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Emotion Detection from Multilingual Text and Multi-Emotional Sentence using Difference NLP Feature Extraction Technique and ML Classifier.


Affiliations
1 Dept. ofComputer Science and Engineering, International Islamic University Chittagong, Chittagong., Bangladesh
 

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 detectingexact 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.

Keywords

Emotion, Machine Learning, Multi-Emotional Sentence, Multi-Lingual, NLP, Bangla.
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  • Emotion Detection from Multilingual Text and Multi-Emotional Sentence using Difference NLP Feature Extraction Technique and ML Classifier.

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Authors

Shahidul Islam Khan
Dept. ofComputer Science and Engineering, International Islamic University Chittagong, Chittagong., Bangladesh
FaisalBin Aziz
Dept. ofComputer Science and Engineering, International Islamic University Chittagong, Chittagong., Bangladesh
MdMisbah Uddin
Dept. ofComputer Science and Engineering, International Islamic University Chittagong, Chittagong., Bangladesh

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 detectingexact 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.

Keywords


Emotion, Machine Learning, Multi-Emotional Sentence, Multi-Lingual, NLP, Bangla.

References