EmoTweet-Malay-6: A Malay Tweet Corpus for Textual Emotion Analysis

Textual emotion detection has gained unprecedented interest over the years due to its wide practical applications, but existing emotion corpora are skewed toward English. Malay, a resource-poor language, currently does not have a reliable emotion corpus that can be used as a benchmark, particularly in the variation of the Malay language used in Malaysia. We present EmoTweet-Malay-6, the first carefully curated Malay emotion corpus containing 5526 tweets annotated with six emotion categories: anger, fear, happiness, love, sadness, surprise and none (no emotion). Since Malaysians are typically at least bilingual, a fraction of this corpus is also code-switched (English and Malay). The tweets were initially annotated using a rule-based classifier following the original source, but we enhanced the corpus by providing additional human validation. Three native Malay speakers were trained to annotate these tweets and achieved Fleiss’ kappa of 0.604. We benchmarked EmoTweet-Malay-6 using commonly adopted machine learning, deep learning, and pre-trained language models. The Malay emotion classifiers serving as baselines show good performance with a macro [Formula: see text]1-score ranging between 0.81 to 0.95 across all six emotions. EmoTweet-Malay-6 serves as an essential resource to advance the development and study of emotion detection in the Malay language.