{
 "id": "SEACrowd/prdect_id",
 "owner": "SEACrowd",
 "name": "prdect_id",
 "downloads": 62,
 "likes": 0,
 "last_modified": "2024-06-24T13:27:16.000Z",
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 "licence_link": null,
 "file_licence": "cc-by-4.0",
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 ],
 "sources": [],
 "gated": false,
 "tasks": [
  "sentiment-analysis",
  "emotion-classification"
 ],
 "citation": "@article{SUTOYO2022108554,\ntitle = {PRDECT-ID: Indonesian product reviews dataset for emotions classification tasks},\njournal = {Data in Brief},\nvolume = {44},\npages = {108554},\nyear = {2022},\nissn = {2352-3409},\ndoi = {https://doi.org/10.1016/j.dib.2022.108554},\nurl = {https://www.sciencedirect.com/science/article/pii/S2352340922007612},\nauthor = {Rhio Sutoyo and Said Achmad and Andry Chowanda and Esther Widhi Andangsari and Sani M. Isa},\nkeywords = {Natural language processing, Text processing, Text mining, Emotions classification, Sentiment analysis},\nabstract = {Recognizing emotions is vital in communication. Emotions convey\nadditional meanings to the communication process. Nowadays, people can\ncommunicate their emotions on many platforms; one is the product review. Product\nreviews in the online platform are an important element that affects customers’\nbuying decisions. Hence, it is essential to recognize emotions from the product\nreviews. Emotions recognition from the product reviews can be done automatically\nusing a machine or deep learning algorithm. Dataset can be considered as the\nfuel to model the recognizer. However, only a limited dataset exists in\nrecognizing emotions from the product reviews, particularly in a local language.\nThis research contributes to the dataset collection of 5400 product reviews in\nIndonesian. It was carefully curated from various (29) product categories,\nannotated with five emotions, and verified by an expert in clinical psychology.\nThe da",
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}