EmoTweetID: A Dataset of Indonesian Tweets for Emotion Classification and Word Embedding Construction
The EmoTweetID dataset is a large-scale open resource of Indonesian tweets curated for emotion classification and word embedding, addressing the scarcity of publicly available datasets for this low-resource language. Tweets were collected from the X platform (formerly Twitter) using the snscrape library, yielding more than 4.5 million raw tweets based on basic and derived emotion keywords from Ekman’s six basic emotions. After cleaning to remove duplicates, irrelevant tweets, and sensitive elements, a corpus of 3,126,987 clean unlabeled tweets was compiled from the first scraping stage. The second stage gathered 2,243 clean tweets annotated through lexicon-based and manual methods, into six emotion classes based on Ekman’s basic emotions: anger, disgust, fear, joy, sadness, and surprise. The manual annotation by three psychology students used majority voting and achieved substantial inter-annotator agreement, with a Fleiss’ Kappa score of 0.7323. Two pretrained embeddings, Word2Vec and fastText, were trained on the corpus using a 300-dimensional skip-gram architecture to enrich semantic representation. Baseline evaluations using BiLSTM with fastText achieved a weighted F1-score of 0.8285 on the human-annotated set, demonstrating the practical utility of the dataset and embeddings for the downstream emotion classification task. Publicly available on Mendeley Data, the EmoTweetID dataset provides a valuable foundation for advancing Indonesian natural language processing by enabling unsupervised pre-training and supervised multi-class emotion classification.
Authors:
Kuncahyo Setyo Nugroho, Fitra Abdurrachman Bachtiar, Wayan Firdaus Mahmudy, Matthew Martianus Henry, Mahmud Isnan, Gusti Pangestu, Bens Pardamean
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