Emotion Classification on Indonesian Twitter Dataset

The rapid growth of Twitter usage attracts many researchers to utilize Twitter data for several purposes, including emotion analysis. However, there is a resource limitation in standard dataset for emotion analysis task for under-resourced language, especially Indonesian. In this study, we build an Indonesian twitter dataset for emotion classification task which is publicly available. In addition, we conduct feature engineering to decide the best feature in emotion classification. The features used in this research are lexicon-based, Bag-of-Words, word embeddings, orthography and Part-Of-Speech (POS)tag features. We test those features in two datasets with different characteristics. F1-score is employed as an evaluation metric. The results of our experiments show that implementing the combination of all proposed features in our built dataset can achieve 69.73% of F1-Score, which outperforms the baseline model by 26.64%.