<![CDATA[The Cash on Delivery (COD) service provided by the Shopee e-commerce platform often elicits a large volume of user reviews that exhibit unconventional language structures, prompting the need for a precise and automated sentiment analysis mechanism. This research endeavor seeks to categorize sentiments expressed in Shopee reviews as either positive or negative by leveraging the Support Vector Machine (SVM) algorithm, which has been fine-tuned using Particle Swarm Optimization (PSO). A key obstacle in text analysis lies in the vast feature space, which can impair model efficacy. Thus, PSO is utilized as a feature selection technique to identify the most pertinent set of terms from the TF-IDF feature extraction. The findings reveal that the integration of PSO successfully decreased feature dimensionality by 45% from the initial set of 1,000 features. Despite the substantial reduction in features, the SVM-PSO model achieved an enhanced accuracy of 81.21%, surpassing the baseline model's 78.79%. With an AUC value of 0.845, it is evident that the model retains stability and effectiveness in discerning sentiment even with a considerably reduced feature set. This investigation illustrates the efficacy of PSO optimization in eliminating extraneous features and refining the model's focus on sentiment-carrying vocabulary.]]>
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