oneonlee/cleansed_emocontext|情感分析数据集|文本分类数据集

该数据集名为cleansed_emocontext，是一个经过清洗和标准化的文本情感分类数据集。它源自emo数据集，专注于通过分析对话中的前两轮内容来推断当前话语的情感，情感类别包括快乐、悲伤、愤怒和其他。数据集包含对话的turn1、turn2、turn3以及情感标签label。该数据集主要用于文本分类任务，特别是情感分类，支持的情感类别有四种：快乐、悲伤、愤怒和其他。数据集语言为英语，数据量在1万到10万条之间。

原始信息汇总

数据集概述

• 数据集名称: Cleansed_EmoContext
• 数据集别名: cleansed_emo2019
• 数据集大小: 10K<n<100K
• 语言: 英语 (en)
• 任务类别: 文本分类
• 具体任务: 情感分类
• 许可: MPL-2.0
• 标签创建者: 专家生成
• 语言创建者: 众包
• 源数据集: Emo
• 数据集特征:
  • turn1, turn2, turn3: 字符串类型
  • label: 分类标签，包括 "others" (0), "happy" (1), "sad" (2), "angry" (3)
• 数据分割:
  • 训练集: 30160条数据
  • 测试集: 5509条数据

数据集详情

• 数据集描述: 该数据集是一个清洗和标准化版本的Emo数据集，用于文本对话中的情感分类任务。每个实例包含一个文本对话及其前两轮的上下文，目标是推断出该对话的情感类别，包括快乐、悲伤、愤怒和其他。
• 数据集结构: 数据集包含三个文本字段（turn1, turn2, turn3）和一个标签字段（label），标签字段包含四个可能的情感类别。
• 数据集创建: 数据集的清洗和标准化过程使用了特定的Python脚本，该脚本基于官方EmoContext GitHub提供的代码进行了修改。

引用信息

@inproceedings{chatterjee-etal-2019-semeval, title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text}, author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal}, booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation}, year={2019}, address={Minneapolis, Minnesota, USA}, publisher={Association for Computational Linguistics}, url={https://www.aclweb.org/anthology/S19-2005}, doi={10.18653/v1/S19-2005}, pages={39--48}, abstract={In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading Why dont you ever text me! we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class} }