物流投诉工单分类语料集

本数据集聚焦物流投诉场景，覆盖时效延迟、虚假操作、货品破损、丢件漏件、错派错发、客服服务差、费用争议、理赔纠纷、上门服务等九大类投诉主题，每类包含丰富的细分场景模板。数据集每条语料包含标题和正文，标题为投诉事项概括，正文模拟用户投诉描述。所有数据均附带细粒度标注，包括文本属性、作者画像、情感倾向、适用场景等高价值维度，可直接用于文本分类、情感分析、投诉工单自动分类等NLP任务。数据分布模拟真实物流投诉场景，负面情感占比约80%，符合投诉场景的实际特征。This dataset focuses on logistics complaint scenarios, covering nine core complaint themes: delivery delay, false operations, damaged goods, lost/missing items, mis-delivery/mis-sending, poor customer service, fee disputes, claim disputes, and door-to-door service-related issues. Each category contains abundant fine-grained scenario templates. Each corpus in the dataset consists of a title and a body: the title serves as a concise summary of the complaint matter, while the body simulates the user's detailed complaint description. All data are equipped with fine-grained annotations covering high-value dimensions including text attributes, author portraits, sentiment tendencies, applicable scenarios and more, which can be directly applied to NLP tasks such as text classification, sentiment analysis and automatic classification of complaint work orders. The data distribution simulates real-world logistics complaint scenarios, with negative sentiment accounting for approximately 80%, which aligns with the actual characteristics of such complaint scenarios.

搜集汇总

背景与挑战

背景概述

该数据集专注于物流投诉场景，涵盖时效延迟、虚假操作等九大类投诉主题，每条语料均包含标题和正文，并附带文本属性、情感倾向等细粒度标注。数据为虚拟生成，模拟真实投诉分布，负面情感占比约80%，适用于文本分类、情感分析等自然语言处理任务。