Sentiment analysis motivational message for learning activity in Bahasa Indonesia

Social networks are usually used for giving a sentiment such as opinion, complaining, and complementing about something. It gives the possibility that there will be a message that motivates the learners on study. This paper proposes a method to classify the messages from a social network which is likely to motivate for learning activity. We conduct sentiment analysis before the corpus is accessed by the users to labeling whether the messages are likely to motivate learner or not. This way is useful because it will cut a lot of messages that are inappropriate and avoid the messages in foreign languages. The result showed that the rate of correct estimations is higher than the work that applied discriminative classifier.