Automatic mood classification of Indonesian tweets using linguistic approach
Research concerning Twitter mining becomes an interesting research topic recently. It is proven by numerous number of published paper related with this topic. This research is intended to develop a prototype system for classifying Indonesian language tweets. The prototype includes preprocessing step, main information retrieval and classification system. This research proposes a system that uses grammatical rule for retrieving main information from the tweet, and then classifies the information to the suitable mood space. The classification algorithm, which is used, is lexicon based classifier. The proposed classification system has 53.67% accuracy for classifying tweets into 12 mood spaces and 75% accuracy for classifying tweets into 4 mood spaces. The proposed lexicon classifier yields better result than SVM and Na├¤ve Bayes classifier. The additional main phrase detection also has a role for increasing the accuracy rate for around 20%.
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