Leveraging Convolutional Neural Networks and Random Forests for Advanced Sentiment Classification of Social Media Responses on Public Services
DOI:
https://doi.org/10.30871/jaic.v10i1.11965Keywords:
Sentiment Analysis, Convolutional Neural Network (CNN), Random Forest Classification, Continuous Bag-of-Word (CBOW)Abstract
In the digital era, social media has become a significant channel for citizens to express their opinions on government services. In Indonesia, particularly in the context of municipal issues, understanding public sentiment is essential to improving public service delivery. This study analyzes user comments from Facebook, Instagram, Twitter, and YouTube to capture public responses toward local government performance. Departing from previous studies that typically employ binary or three-level classifications, this research implements a five-category sentiment scheme: Very Good, Good, Fair, Poor, and Very Poor. A hybrid model combining a Convolutional Neural Network (CNN) for feature extraction and a Random Forest (RF) classifier is proposed to address this multi-class task. The model achieves 87% accuracy, outperforming the individual CNN and RF models. The results demonstrate the potential of social media–based sentiment analysis to enhance public service quality in Indonesia.
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