Sentimen Analisis Kegiatan Trading Pada Ap-likasi Twitter dengan Algoritma SVM, KNN Dan Random Forrest - Wardani, Neng Resti; Saepudin, Sudin; Warman, Cecep (J-SAKTI (Jurnal Sains Komputer dan Informatika), 2022)
This study aims to find out how people comment on trading activities that are currently busy. As we know that lately there have been cases of trading involving affiliates, many people feel that they have been deceived by these activities. From this case, we conducted research using data collection methods regarding trading, which were taken from the Twitter social media platform using the Orange application. The data obtained through the scraping process will then be filtered to separate positive and negative sentiments, so that the data ready for sentiment analysis is 1,400 tweets. Data were analyzed using three methods, namely Random Forest, KNN, and SVM (Support Vector Machines). The results obtained from the research conducted which has 3 variables, namely positive sentiment has a value of 29%, negative is 10%, and neutral has a value of 62%. To analyze sentiment data from Twitter the author uses 3 classification methods and produces an accuracy value of KnN of 0.999, Random forest 0.994 and Naïve SVM 0.992. Based on the results of the analysis that has been carried out regarding trading activities, people think that not all trading is illegal and fraudulent because many sites are still legal