Emotion Detection in Online Social networks: Using Deep Learning Approach

Khalid, Tarik; Djibrine, Mahamat; Mohamed, Hafso

dc.contributor.author	Khalid, Tarik
dc.contributor.author	Djibrine, Mahamat
dc.contributor.author	Mohamed, Hafso
dc.date.accessioned	2023-04-28T05:45:18Z
dc.date.available	2023-04-28T05:45:18Z
dc.date.issued	2022-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1863
dc.description	Supervised by Ms. Lutfun Nahar Lota, Asst. Professor, Department of Computer Science and Engineering(CSE), Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.	en_US
dc.description.abstract	Emotion recognition is one of the most difficult jobs in the Natural Language Processing (NLP) sector since it relies significantly on contextual information and mixed emotions in a sentence during the emotion detection process. Therefore, we propose two deep learning approaches CNN and Bi-LSTM, we built these two models on a dataset that contains six levels of emotions. The two models have proven to give good accuracy above 90% on this dataset. From that, we have decided to try them out on thirteen levels of emotions to see if we can still achieve reasonable performance on a high level of emotions	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	K-NN, CNN, Bi-LSTM, Deep Learning, Naive Bayes, SVM	en_US
dc.title	Emotion Detection in Online Social networks: Using Deep Learning Approach	en_US
dc.type	Thesis	en_US