Analisis Sentimen Impor Beras 2018 Pada Twitter Menggunakan Metode Support Vector Machine dan Pembobotan Jumlah Retweet - Renaza Afidianti Nandini; Yuita Arum Sari; Putra Pandu Adikara (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Social media Twitter is one of the largest real time databases and is very useful for knowing people's perceptions in Indonesia. The issue of rice import polemic on Twitter tweets is an important thing to study as text processing. This study discusses sentiment analysis on 2018 rice import Twitter using the Support Vector Machine (SVM) method and Weighting the Number of Retweets. The use of the weighting feature of the number of retweets uses a comparison of certain constants (Î± and Î²) 11 times to obtain the results of positive and negative class analysis. The data used in this study were 318 data consisting of two types of data namely training data and test data with a ratio of 70% training data and 30% test data. From the results of accuracy testing using the Support Vector Machine method without weighting the number of retweets by 50.00%, precision by 49.46%, recall by 97.87%, and f-measure by 65.71%. Accuracy testing results using the Support Vector Machine method with a weighting of retweet amount of 50.00%, precision of 49.46%, recall of 01.00% and f-measure of 65.73%. It can be concluded that the use of the weighting feature of the number of retweets can provide optimal results and is able to classify sentiment analysis.