Sentimental analysis on political speeches

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

Bibliographic Details
Main Authors:	Atiq, Asif, Abeed, Abrar Shahriar, Efat, Azher Ahmed, Momin, Armanul
Other Authors:	Alam, Md. Golam Rabiul
Format:	Thesis
Language:	English
Published:	Brac University 2022
Subjects:	Political speeches Sentiment analysis Context analysis LDA topic modeling Longformer Ensemble learning LDA Algorithm Topic modeling
Online Access:	http://hdl.handle.net/10361/16591

id	10361-16591
record_format	dspace
spelling	10361-165912022-05-11T21:01:39Z Sentimental analysis on political speeches Atiq, Asif Abeed, Abrar Shahriar Efat, Azher Ahmed Momin, Armanul Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Political speeches Sentiment analysis Context analysis LDA topic modeling Longformer Ensemble learning LDA Algorithm Topic modeling This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 24-25). Politics is an essential part of human society. From the start of human civilization, politics has controlled every human society. Political speeches have had one of the most influential roles in shaping the world. Speeches of the written variety have been etched in history. These sorts of speeches have a great effect on the general people and their actions in the coming few days. With advancing technologies, people from all across the world get to listen to these speeches hence the impact on the listener is increasing on a global scale. We analyzed the performance of different models on our corpus of speeches using sentiment and context analysis and then we compared the results of those models to see the difficulty in analyzing sentiment and context of speeches of country leaders. In our research we have focused on the presidents/prime ministers of the five permanent members of the United Nations Security Council which are France, China, Russia, United Kingdom and United States. Moreover, if left unchecked, a political personnel or party may cause major problems. In many cases there may be a warning sign that the government needs to change their policies and also listen to the people. By classifying the speeches into positive, negative or neutral categories in terms of sentiment and five context categories international, nationalism, development, extremism and others and evaluated the accuracy of our models. By using approaches such as Longformer (RoBERTa based model), TF-IDF with ensemble learning models and LDA topic modeling along with ensemble learning models, we were able to achieve some satisfactory results. We have used a modified Bidirectional Encoder Representations from Transformers (BERT) algorithm which is Longformer and TF-IDF with ensemble learning models for sentiment analysis and an LDA based topic model implemented on ensemble learning models to analyze our speeches for context analysis. We have achieved a 0.67 score on the accuracy of Sentiment and we also achieved a 0.67 accuracy on contexts. Asif Atiq Abrar Shahriar Abeed Azher Ahmed Efat Armanul Momin B. Computer Science 2022-05-11T05:26:59Z 2022-05-11T05:26:59Z 2022 2022-01 Thesis ID 18101556 ID 18101257 ID 18101027 ID 17101281 http://hdl.handle.net/10361/16591 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. 25 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Political speeches Sentiment analysis Context analysis LDA topic modeling Longformer Ensemble learning LDA Algorithm Topic modeling
spellingShingle	Political speeches Sentiment analysis Context analysis LDA topic modeling Longformer Ensemble learning LDA Algorithm Topic modeling Atiq, Asif Abeed, Abrar Shahriar Efat, Azher Ahmed Momin, Armanul Sentimental analysis on political speeches
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Atiq, Asif Abeed, Abrar Shahriar Efat, Azher Ahmed Momin, Armanul
format	Thesis
author	Atiq, Asif Abeed, Abrar Shahriar Efat, Azher Ahmed Momin, Armanul
author_sort	Atiq, Asif
title	Sentimental analysis on political speeches
title_short	Sentimental analysis on political speeches
title_full	Sentimental analysis on political speeches
title_fullStr	Sentimental analysis on political speeches
title_full_unstemmed	Sentimental analysis on political speeches
title_sort	sentimental analysis on political speeches
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/16591
work_keys_str_mv	AT atiqasif sentimentalanalysisonpoliticalspeeches AT abeedabrarshahriar sentimentalanalysisonpoliticalspeeches AT efatazherahmed sentimentalanalysisonpoliticalspeeches AT mominarmanul sentimentalanalysisonpoliticalspeeches
_version_	1814309865467674624

Sentimental analysis on political speeches

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