PUBLIC SENTIMENT ANALYSIS ON ELECTRIC CARS USING MACHINE LEARNING ALGORITMS - Damaiarta Tejayanda, Rigger; Prasetyo, Bayu; Faisal, Muhamad Agus; Abigael, Rakha; Rohana, Tatang; Sukmawati, Cici Emilia (Jurnal Teknik Informatika (Jutif), 2024)
The presence of electric vehicles has generated diverse opinions among the public, as widely discussed on social media. The lack of understanding about electric vehicle innovation can influence their perception. Issues such as infrastructure, high prices, pollution concerns, and adaptation to new technology present challenges for automotive companies in their innovation efforts. This study aims to analyze public sentiment towards electric vehicles through comments on the TikTok platform, which can serve as a reference for companies in evaluating and developing electric vehicle innovations. Six different classification algorithms were tested to determine the most effective and accurate one. The methods used include data collection of comments, pre-processing, data processing through stemming, tokenization, and stopwords removal techniques, as well as labeling and modeling stages. The results of the study show that Support Vector Machine are the most superior algorithms with the highest accuracy of 90%. This research provides new insights into public perception of electric cars and the effectiveness of various sentiment analysis algorithms in the context of social media.