<![CDATA[Many researchers currently employ supervised, machine learning methods to study sentiment analysis. Analysis can be done on movie reviews, Twitter reviews, online product reviews, blogs, discussion forums, Myspace comments, and social networks. Support Vector Machines (SVM) classifiers are used to analyze the Twitter data set using different parameters. The analysis and discussion were undertaken to allow for the conclusion that SVM has been successfully implemented utilizing the IMDb data for this study (Support Vector Machine). To complete this study, the preprocessing phase, which consisted of filtering and classifying data using SVM with a total of 50.000 data points, was completed after collecting up to 40.000 reviews to use as training data and 10.000 reviews to use as testing data. 25.000 positive and 25.000 negative points make up the view. In this study, we adopted an evaluation matrix including accurate, precision, recall, and F1-score. According to the experiment report, our model achieved SVM with Bags of Word (BoW) used to get results for the highest accuracy test, which was 88,59% accurate. Then, using grid-search, optimize against the SVM parameters to find the best parameters that SVM models can use. Our model achieved Term Frequency–inverse Document Frequency (TF-IDF) was used to get results for the highest accuracy test, which was 91,27% accurate.]]>
