地球资源数据云——数据资源详情
PRDECT-ID：印尼语情绪分类
该数据集《PRDECT - ID: Indonesian Emotion Classification》主要用于多分类任务，数据形态以图像为主，应用场景偏向医疗健康。 题目说明：Annotated Reviews for Emotion Analysis 任务类型：图像多分类。 建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：未检测到标准 CSV，可优先查看目录中的索引或说明文件。 Description PRDECT - ID Dataset is a collection of Indonesian product review data annotated with emotion and sentiment labels. The data were collected from one of the giant e - commerce in Indonesia named Tokopedia. The dataset contains product reviews from 29 product categories on Tokopedia that use the Indonesian language. Each product review is annotated with a single emotion, i.e., love, happiness, anger, fear, or sadness. The group of annotators does the annotation process to provide emotion labels by following the emotions annotation criteria created by an expert in clinical psychology. Other attributes related to the product review are also extracted, such as Location, Price, Overall Rating, Number Sold, Total Review, and Customer Rating, to support further research. Categories
摘要概览
该数据集《PRDECT - ID: Indonesian Emotion Classification》主要用于多分类任务，数据形态以图像为主，应用场景偏向医疗健康。 题目说明：Annotated Reviews for Emotion Analysis
任务类型：图像多分类。
建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：未检测到标准 CSV，可优先查看目录中的索引或说明文件。
Description
PRDECT - ID Dataset is a collection of Indonesian product review data annotated with emotion and sentiment labels. The data were collected from one of the giant e - commerce in Indonesia named Tokopedia. The dataset contains product reviews from 29 product categories on Tokopedia that use the Indonesian language.
Each product review is annotated with a single emotion, i.e., love, happiness, anger, fear, or sadness. The group of annotators does the annotation process to provide emotion labels by following the emotions annotation criteria created by an expert in clinical psychology.
Other attributes related to the product review are also extracted, such as Location, Price, Overall Rating, Number Sold, Total Review, and Customer Rating, to support further research.
Categories