A Deep Learning Approach to Detect Depression from Bengali Text

Author:

Rafidul Hasan Khan Md.,Afroz Umme Sunzida,Masum Abu Kaisar Mohammad,Abujar Sheikh,Hossain Syed Akhter

Publisher

Springer Singapore

Reference19 articles.

1. Liu, B., Synthesis Lectures on Human Language Technologies: Sentiment analysis and opinion mining 5(1), 1–167 (2012)

2. Kanakaraj M., Guddeti, R.M.R.: NLP based sentiment analysis on Twitter data using ensemble classifiers. In: 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), Chennai, 2015, pp. 1–5

3. Ortigosa, A., Mart, J.M., Carro, M.: Sentiment analysis in Facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014)

4. Mostafa, M.M.: More than words: social networks text mining for consumer brand sentiments. Expert Syst. Appl. 40(10), 4241–4251 (2013)

5. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113, 2014. Available: https://doi.org/10.1016/j.asej.2014.04.011

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1. Multilingual Depression Detection in Online Social Media Across Eight Indian Languages;2024 3rd International Conference for Innovation in Technology (INOCON);2024-03-01

2. Detecting Level of Depression from Social Media Posts for the Low-resource Bengali Language;Journal of Engineering Advancements;2023-06-28

3. A Comprehensive Roadmap on Bangla Text-based Sentiment Analysis;ACM Transactions on Asian and Low-Resource Language Information Processing;2023-04-06

4. Sentence Level Sentiment Classification Using Machine Learning Approach in the Bengali Language;2022 International Conference on Decision Aid Sciences and Applications (DASA);2022-03-23

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