Year: 2025
Type: article
Abstract: Public opinion plays a crucial role in evaluating the performance and effectiveness of governance. With the rapid growth of social media, citizens freely express satisfaction, criticism, and expectations regarding government policies. This research proposes a deep learning-based sentiment analysis framework to quantify and interpret public perception of governance. Using a curated dataset from social platforms, news portals, and public forums, the study implements LSTM, Bi-LSTM, and BERT to classify sentiments into positive, negative, and neutral categories. Experimental results show that transformer-based models outperform recurrent neural architectures, achieving an accuracy of 92.6%. The findings highlight how deep learning can provide actionable insights for policy formulation, crisis response, and public communication strategies.
Authors: Harshil Trivedi, Samuel Patel
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4417154691
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