Abstract
Most of the dimensional sentiment analysis methods are established on deep learning algorithms in natural language processing which can categorize the sentiment of the Bengali text or paragraph by creating a definite pole. Our purpose is to identify the depression-related ‘sad’ post using the above method from the Bengali dataset. To implement this work, we have collected Bengali text from different platforms such as social media, Bengali blogs, and quotes of Noble persons. And to classify the sentiment as happy or sad from those texts by using our model. The data preprocessing of Bengali text is one of the toughest parts of this model. For tokenizing the data to train the model, we have used Keras tokenizer. During this experiment, we have applied a recurrent neural network with a long short-term memory algorithm and achieved 98% accuracy and also able to detect the sentiment from the given dataset.
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Acknowledgements
We really obliged to accept their assistance from DIU NLP and Machine Learning Research Lab for giving GPU’s support. We delighted, Dept. of Computer Science and Engineering, Daffodil International University for supporting us. And also pleased to anonymous reviewers for their worthy explanation and feedback.
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Rafidul Hasan Khan, M., Afroz, U.S., Masum, A.K.M., Abujar, S., Hossain, S.A. (2021). A Deep Learning Approach to Detect Depression from Bengali Text. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1300. Springer, Singapore. https://doi.org/10.1007/978-981-33-4367-2_74
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DOI: https://doi.org/10.1007/978-981-33-4367-2_74
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