CFP last date
20 January 2025
Reseach Article

Analysis of the Opinion of the People of Bangladesh on the Padma Setu Megaproject

by Tamim Al Mahmud, Sazeda Sultana, Tanjin Irfan Chowdhury
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 18
Year of Publication: 2023
Authors: Tamim Al Mahmud, Sazeda Sultana, Tanjin Irfan Chowdhury
10.5120/ijca2023922896

Tamim Al Mahmud, Sazeda Sultana, Tanjin Irfan Chowdhury . Analysis of the Opinion of the People of Bangladesh on the Padma Setu Megaproject. International Journal of Computer Applications. 185, 18 ( Jun 2023), 8-14. DOI=10.5120/ijca2023922896

@article{ 10.5120/ijca2023922896,
author = { Tamim Al Mahmud, Sazeda Sultana, Tanjin Irfan Chowdhury },
title = { Analysis of the Opinion of the People of Bangladesh on the Padma Setu Megaproject },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 18 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number18/32794-2023922896/ },
doi = { 10.5120/ijca2023922896 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:25.259133+05:30
%A Tamim Al Mahmud
%A Sazeda Sultana
%A Tanjin Irfan Chowdhury
%T Analysis of the Opinion of the People of Bangladesh on the Padma Setu Megaproject
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 18
%P 8-14
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment analysis is the term used to describe the process of mining people’s opinions or emotions. The public opinion on Padma Bridge was researched and documented in this research paper. Considering the results, the Bangladeshi government can easily decide on future large construction projects. The construction of the Padma bridge is an important milestone for Bangladesh because to the high budget and prohibited World Bank funding. On YouTube, Facebook and other social online news, Bangladeshis share their thoughts, feelings, suggestions, and opinions about the Padma Bridge project. The main objective of the research is sentiment analysis of Padma Bridge sentiment based on the Bangla comment dataset. We collected over 10,000 data with two categories of sentiment: positive and negative. The innovative voting method proposed in this paper is a significant breakthrough in the field of sentiment analysis. Our approach can compare and count the sentiments generated by different Machine Learning and Deep Learning models, resulting in a decision-making process that is more accurate and consistent. Our model considers the strengths and weaknesses of each individual model, ensuring that the final decision is based on the maximum voting results. Our research shows that approach outperforms every machine learning and deep learning model by about 6.5% in terms of accuracy. This improvement is significant and has practical im- plications for industries such as marketing, finance, and politics, where accurate sentiment analysis is crucial for decision-making. Our approach has the potential to revolutionize sentiment analysis by providing a more robust and accurate method for analyzing large volumes of data. Further research could explore ways to optimize this approach even further, making it even more effective in real world applications.

References
  1. N. Tabassum and M. I. Khan, "Design an Empirical Framework for Sentiment Analysis from Bangla Text using Machine Learning," 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox'sBazar, Bangladesh, 2019, pp. 1-5, https://doi.org/10.1109/ECACE.2019.8679347
  2. M. F. Wahid, M. J. Hasan and M. S. Alom, "Cricket Sentiment Analysis from Bangla Text Using Recurrent Neural Network with Long Short Term Memory Model," 2019 International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, Bangladesh, 2019, pp. 1-4, https://doi.org/10.1109/ICBSLP47725.2019.201500
  3. M. A. -U. -Z. Ashik, S. Shovon and S. Haque, "Data Set For Sentiment Analysis On Bengali News Comments And Its Baseline Evaluation," 2019 International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, Bangladesh, 2019, pp. 1-5, https://doi.org/10.1109/ICBSLP47725.2019.201497
  4. T. A. Mahmud, S. Sultana, T. I. Chowdhury and F.R. Anando, “A New Approach to Analysis of Public Sentiment on Padma Bridge in Bangla Text,” 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, 2022, pp. 1-6, https://doi.org/10.1109/STI56238.2022.10103315
  5. M. G. Hussain, T. A. Mahmud and W. Akthar, “An Approach to Detect Abusive Bangla Text,” 2018 International Conference on Innovation in Engineering and Technology (ICIET), Dhaka, Bangladesh, 2018, pp. 1-5, https://doi.org/10.1109/CIET.2018.8660863
  6. M. G. Hussain, S. Kabir, T. A. Mahmud, A. Khatun and M. J. Islam, “Assessment of Bangla Descriptive Answer Script Digitally,” 2019 International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, Bangladesh, 2019, pp. 1-4, https://doi.org/10.1109/ICBSLP47725.2019.202042
  7. T. Ahmed, S. F. Mukta, T. Al Mahmud, S. A. Hasan and M. Gulzar Hussain, “Bangla Text Emotion Classification using LR, MNB and MLP with TF-IDF CountVectorizer,” 2022 26th International Computer Science and Engineering Conference (ICSEC), Sakon Nakhon, Thailand, 2022, pp. 275-280, https://doi.org/10.1109/ICSEC56337.2022.10049341
  8. Md Gulzar Hussain, Tamim Al Mahmud. (2019). A Technique for Perceiving Abusive Bangla Comments. GREEN UNIVERSITY OF BANGLADESH JOURNAL OF SCIENCE AND ENGINEERING, 04(01). https://doi.org/10.5281/zenodo.3544583
  9. Kumar, T. Praveen, and B. Vishnu Vardhan. “A Pragmatic Approach to Emoji based Multimodal Sentiment Analysis using Deep Neural Networks.” Journal of Algebraic Statistics 13.1 (2022): 473-482. https://doi.org/10.52783/jas.v13i1.108
  10. Aliman, G.B., Nivera, T.F.S., Olazo, J.C.A., Ramos, D.J.P., Sanchez, C.D.B., Amado, T.M., Arago, N.M., Jorda Jr, R.L., Virrey, G.C. and Valenzuela, I.C., 2022. Sentiment Analysis using Logistic Regression. Journal of Computational Innovations and Engineering Applications JULY 2022, 35, p.40. https://www.dlsu.edu.ph/wp-content/uploads/pdf/research/journals/jciea/vol-7-1/4aliman.pdf
  11. Salinca, Andreea. (2015). Business Reviews Classification Using Sentiment Analysis. 247-250. https://doi.org/10.1109/SYNASC.2015.46.
  12. Al-Adhaileh, Mosleh & Aldhyani, Theyazn & Alghamdi, Ans. (2022). Online Troll Reviewer Detection Using Deep Learning Techniques. Applied Bionics and Biomechanics. 2022. 1-10. https://doi.org/10.1155/2022/4637594
  13. Iram, M. ., Rehman, S. U., Shahid, S. ., & Mehmood , S. A. . (2022). Anatomy of Sentiment Analysis of Tweets Using Machine Learning Approach : Anatomy of Sentiment Analysis of Tweets. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 59(2), 61–73. https://doi.org/10.53560/PPASA(59-2)771
  14. Ibrahim Abu Farha and Walid Magdy. 2020. From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pages 32–39, Marseille, France. European Language Resource Association. https://aclanthology.org/2020.osact-1.5
  15. Hichem Rahab, Abdelhafid Zitouni, and Mahieddine Djoudi. 2021. SANA: Sentiment analysis on newspapers comments in Algeria. J. King Saud Univ. Comput. Inf. Sci. 33, 7 (Sep 2021), 899–907. https://doi.org/10.1016/j.jksuci.2019.04.012
  16. Tamim Al Mahmud, Md Gulzar Hussain, Sumaiya Kabir, Hasnain Ahmad, and Mahmudus Sobhan. 2020. A Keyword Based Technique to Evaluate Broad Question Answer Script. In Proceedings of the 2020 9th International Conference on Software and Computer Applications (ICSCA 2020). Association for Computing Machinery, New York, NY, USA, 167–171. https://doi.org/10.1145/3384544.33846048
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment SVM RF LSTM Word Cloud Pre-Processing BoW SVD.