Sentiment popularity - Amazon Mechanical Turk dataset

Dataset re-collected from an original dataset collected by Pang, B., and Lee, L. 2004. "A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts". In Proceedings of the 42nd annual meeting on Association for Computational Linguistics. The dataset presents a binary classification problem, with workers asked to select either positive (1) or negative (0) for a 500 sentences extracted from movie reviews, with gold labels assigned by the website. It contains 10,000 sentiment judgements collected from 143 using the Amazon Mechanical Turk platform. Each row is in the format

本数据集复刻自Pang B与Lee L二人2004年的研究，其原始数据集采集自论文《情感教育：基于最小割的主观性摘要的情感分析》（A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts），该论文刊载于第42届国际计算语言学协会（Association for Computational Linguistics，ACL）年会会议论文集。本数据集为二分类任务，共包含从影评中提取的500个句子，标注人员需为每个句子标注正向（1）或负向（0），金标准标签由对应网站提供。数据集通过亚马逊机械 Turk（Amazon Mechanical Turk）平台招募143名标注者完成标注，总计收集得到10000条情感标注结果。每条数据行的格式为

搜集汇总

背景与挑战

背景概述

该数据集是一个用于情感分析二元分类的亚马逊Mechanical Turk数据集，源自Pang和Lee在2004年的电影评论研究，包含10,000个情感判断，由143名工人对500个句子进行正面或负面标签标注。它主要用于训练和评估情感分类模型，特点是数据规模较大且通过众包平台收集，确保了标签的多样性。