Uddin, JiaAshraf, Faisal BinChowdhury, Ibtehaz AliDewan, EshadJishan, TM Nafi z MahmoodKabir, SamiulMazumder, Joyasish2024-11-132024-11-13©20212021-01ID 16101302ID 16101226ID 16101006ID 16301041ID 16101292http://hdl.handle.net/10361/24786Catalogued from PDF version of thesis.Includes bibliographical references (pages 23-24).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.Sentiment analysis from texts has been a major research eld in NLP. However, most of the studies are on binary (positive and negative) classi cation of the texts. While researching, we found that the accuracy of multi-class text classi cation according to emotions is very low when compared to binary classi cations, as understanding and quantifying emotions is a very di cult task. We studied the two commonly used deep learning models used for text classi cations: Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). We found that the greatest accuracy was achieved when the CNN model is used combined with a LSTM. In our paper, we proposed an LSTM-CNN hybrid model to classify texts according to ve emotion classes and achieve an accuracy of 65%. We further studied Support Vector Machine (SVM) and Naive-Bayes classi ers. The experimental results show that the LSTM-CNN model had an improved accuracy.36 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Sentiment analysisCNNConvolutional neural networkLSTMNLPText classificationNatural language processing (Computer science).Sentiment analysis--Data processing.Neural networks (Computer science).Thesis