Sentiment analysis on Bangladesh airlines review data using machine learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.

Xehetasun bibliografikoak
Egile nagusia:	Hasib, Khan Md.
Beste egile batzuk:	Alam, Md. Golam Rabiul
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2022
Gaiak:	Bangladesh Airlines Online review Sentiment analysis Topic modeling Deep learning LIME Machine learning Cognitive learning theory (Deep learning)
Sarrera elektronikoa:	http://hdl.handle.net/10361/16666

id	10361-16666
record_format	dspace
spelling	10361-166662022-05-28T08:28:24Z Sentiment analysis on Bangladesh airlines review data using machine learning Hasib, Khan Md. Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Bangladesh Airlines Online review Sentiment analysis Topic modeling Deep learning LIME Machine learning Cognitive learning theory (Deep learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 66-73). A common means of transportation in our everyday lives is air travel. As a result, it's no surprise that more and more customers are posting their airline reviews online. However, in the age of machine learning, it would be much easier to extract millions of pieces of information and knowledge from them if a model was used to polarize and comprehend them. Sentiment analysis may be used to understand people's attitudes or sentiments by utilizing sites that provide opinion-rich data. In this work, we worked on a customized dataset including online reviews for 4 major Bangladesh Airlines, performed a multiclass sentiment analysis, and compared the classi ers. Alongside sentiment analysis, topic modeling is also done to get better decisions based on the actual experiences of other customers who have own with airlines. This method begins with pre-processing procedures used to clean the reviews and balance the review data using the Pegasus model's oversampling mechanism. The analysis was carried out 3 di erent machine learning (Decision Tree, Random Forest, and XGBoost) and 3 di erent deep learning classi cation strategies (CNN, LSTM, BERT). The test set's output is the review sentiment (positive/negative/mixed) using a three-class dataset, and the performance in terms of accuracy is calculated. Based on the results, we have achieved the best accuracy 83% in terms of BERT. The accuracies were determined to compare each categorization technique, and the total sentiment count for all four airlines of Bangladesh was displayed in terms of domestic route, international route and overall route. We comprehend the results acquired from USA airlines Tweets data and demonstrate that our framework is more e cient than the earlier model. Therefore, it is essential to consider whether a sentiment makes a particular prediction. Thus, we then train an interpretable LIME model for the sentiments and the construction of explainable sentiments can have a major advantage. Khan Md. Hasib M. Computer Science and Engineering 2022-05-25T03:46:04Z 2022-05-25T03:46:04Z 2022 2022-02 Thesis ID 20266015 http://hdl.handle.net/10361/16666 en Brac 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. 73 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Bangladesh Airlines Online review Sentiment analysis Topic modeling Deep learning LIME Machine learning Cognitive learning theory (Deep learning)
spellingShingle	Bangladesh Airlines Online review Sentiment analysis Topic modeling Deep learning LIME Machine learning Cognitive learning theory (Deep learning) Hasib, Khan Md. Sentiment analysis on Bangladesh airlines review data using machine learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Hasib, Khan Md.
format	Thesis
author	Hasib, Khan Md.
author_sort	Hasib, Khan Md.
title	Sentiment analysis on Bangladesh airlines review data using machine learning
title_short	Sentiment analysis on Bangladesh airlines review data using machine learning
title_full	Sentiment analysis on Bangladesh airlines review data using machine learning
title_fullStr	Sentiment analysis on Bangladesh airlines review data using machine learning
title_full_unstemmed	Sentiment analysis on Bangladesh airlines review data using machine learning
title_sort	sentiment analysis on bangladesh airlines review data using machine learning
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/16666
work_keys_str_mv	AT hasibkhanmd sentimentanalysisonbangladeshairlinesreviewdatausingmachinelearning
_version_	1814309786966032384

Sentiment analysis on Bangladesh airlines review data using machine learning

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