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
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.