<![CDATA[Social media has become the primary means for the public to express their opinions on a wide range of issues, one of which is smartphone products. The social media platforms used to express opinions include Facebook, Instagram, Twitter, WhatsApp and YouTube. Twitter is one of the most widely used platforms for expressing opinions in the form of comments, which may be positive, negative or neutral. This study aims to classify the sentiment of Twitter users’ comments regarding smartphones using the Support Vector Machine (SVM) algorithm. The research employs a quantitative methodology. The data used consists of comments or text data in CSV format, which is processed through a series of pre-processing steps, including data cleaning, tokenisation, stop-word removal and stemming. This will be followed by sentiment modelling and an evaluation of the model’s performance. The results of the study show that the SVM method achieved an accuracy of 82,28%, a recall of 82,26%, a precision of 82,25%, and an F1-score of 82,25%. Based on these results, it can be concluded that the SVM algorithm performs well in sentiment analysis of social media data, particularly Twitter.]]>
Copyrights © 2026
