PERFORMANCE COMPARISON OF NAIVE BAYES, SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR APPLE VISION PRO SENTIMENT ANALYSIS

Abstract

With the development of spatial computing devices, there arises a need to analyze consumer opinions about products such as the Apple Vision Pro (AVP), a technology that combines augmented reality (AR) and virtual reality (VR). This study aims to analyze consumer opinions on the Apple Vision Pro by utilizing data from the social media platform X. Three algorithms—Random Forest, Support Vector Machine (SVM), and Naïve Bayes—are used in text categorization to identify sentiment trends. Data was collected through a crawling process, resulting in 3,753 entries. After preprocessing and labeling, 2,609 clean data points were obtained, with 1,618 classified as negative and 991 as positive. In sentiment analysis, Random Forest delivered the best performance with an accuracy of 83%, followed by SVM at 80%, and Naïve Bayes at 75%. These results indicate that the Random Forest algorithm is more effective in sentiment categorization related to Apple Vision Pro. This study provides significant contributions to companies in understanding public perceptions and crafting more precise data-driven marketing strategies.