Sentiment analysis of social media data involves extracting valuable
insights from vast amounts of unstructured text. Feature selection
plays a crucial role in enhancing the accuracy and efficiency of
sentiment analysis algorithms. This study proposes the application of
the Ant Colony Optimization (ACO) algorithm for feature selection in
sentiment analysis. ACO is inspired by the foraging behavior of ants
and has been successfully applied to various optimization problems. In
this context, ACO is utilized to select the most informative features from
the dataset, thereby improving the performance of sentiment analysis
models. The contribution of this research lies in the adaptation of ACO
for feature selection in sentiment analysis of social media data. By
leveraging the inherent strengths of ACO, such as its ability to explore
large solution spaces and adapt to dynamic environments, more
accurate sentiment analysis models can be developed. Experimental
results demonstrate that the proposed ACO-based feature selection
approach outperforms traditional methods in terms of classification
accuracy and computational efficiency. The selected features exhibit
strong predictive power, leading to improved sentiment analysis
performance on social media data.