There are four possible sentiment classes for each sentiment label: positive, negative, neutral, and positive-negative. Similar to the CASA dataset, each review is labeled with a single sentiment label for each aspect. The dataset covers ten different aspects of hotel quality. HoASA: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, AiryRooms.We define the task to be a multi-label classification task, where each label represents a sentiment for a single aspect with three possible values: positive, negative, and neutral. The dataset covers six aspects of car quality. CASA: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms.There are three possible sentiments on the SmSA dataset: positive, negative, and neutral The text was crawled and then annotated by several Indonesian linguists to construct this dataset. SmSA: This sentence-level sentiment analysis dataset is a collection of comments and reviews in Indonesian obtained from multiple online platforms.The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happy, love, and sadness EmoT: An emotion classification dataset collected from the social media platform Twitter.There are 12 datasets in IndoNLU benchmark for Indonesian natural language understanding. The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia (Indonesian language).
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