Indonesia China Trade Relations, Social Media and Sentiment Analysis: Insight from Text Mining Technique

Sentiment Analysis (SA) employed for detecting, extracting, and classifying people opinions about an issue. Social media is a channel to show people opinions and thoughts. This study aimed to detect and classify Indonesia public opinion from Twitter written in Indonesia language for trade relations between Indonesia and China topic with text mining techniques. The result was the model that detected and classified sentiment of public opinion into negative, neutral or positive sentiment. The sentiment detected by lexicon-based and rule-based sentiment analysis. VADER was chosen as a tool for sentiment analysis lexicon-based. The text classification process was a training stage for the model. The experiment revealed SVM classifier performed higher accuracy value than Naïve Bayes, 67.28% and 64.68% respectively.