A Weighted Sentiment Dataset for Indonesian Telemedicine Text Classification

A Weighted Sentiment Dataset for Indonesian Telemedicine Text Classification is a collection of Indonesian telemedicine user reviews that are annotated with weight and sentiment labels. The data were collected from user reviews of three telemedicine applications in Indonesia, named Alodokter, Halodoc, and KlikDokter. The dataset contains 19,680 rows of user reviews that used Indonesia language and mixed more than one language. Each user review is annotated manually by two human annotators following annotation guidelines created by psychological expertise. This dataset consists of 6 attributes: platform (application distributor platform), app_name (application name), rating (application user satisfaction score), review (Application user review), sentiment (data label), and weight (user review complexity label). The dataset supports experiments in advanced NLP technologies, including BERT, LSTM, and LLMs, such as a user review sentiment classification task.

面向印尼远程医疗文本分类的加权情感数据集（Weighted Sentiment Dataset for Indonesian Telemedicine Text Classification）是一组标注了权重与情感标签的印尼语远程医疗用户评论集合。该数据集的数据源自印尼三款远程医疗应用的用户评论，分别为Alodokter、Halodoc与KlikDokter。本数据集共包含19680条用户评论，这些评论以印尼语为主，且混杂了多种语言。每条用户评论均由两名人类标注员依照心理学专家制定的标注指南完成手动标注。本数据集包含6个属性字段：platform（应用分发平台）、app_name（应用名称）、rating（应用用户满意度评分）、review（应用用户评论内容）、sentiment（情感标签）以及weight（用户评论复杂度标签）。本数据集可支撑先进自然语言处理（Natural Language Processing, NLP）技术的相关实验，涵盖BERT、LSTM以及大语言模型（Large Language Model, LLM）等，可用于用户评论情感分类任务。