论文标题
分析社交媒体上投诉的强度
Analyzing the Intensity of Complaints on Social Media
论文作者
论文摘要
抱怨是一种言语行为，表达了现实与人类期望之间的负面不一致。虽然先前的研究主要集中于确定存在或投诉的类型，但在这项工作中，我们介绍了计算语言学的第一个研究，即测量文本投诉的强度。从这种角度分析投诉特别有用，因为对某些学位的投诉可能会对公司或组织造成严重后果。我们创建了第一个中国数据集，其中包含3,103篇有关中国社交媒体平台微博的投诉的帖子。然后，使用最佳宽度缩放（BWS）方法对这些帖子进行注释。我们表明，通过具有最佳平方误差为0.11的计算模型可以准确估算投诉强度。此外，我们围绕投诉（包括投诉与情感之间的联系）进行了全面的语言分析，以及中文和英语的人使用的投诉表达式的跨语性比较。我们最终表明，我们的投诉强度得分可以合并，以更好地估计社交媒体上帖子的普及。
Complaining is a speech act that expresses a negative inconsistency between reality and human expectations. While prior studies mostly focus on identifying the existence or the type of complaints, in this work, we present the first study in computational linguistics of measuring the intensity of complaints from text. Analyzing complaints from such perspective is particularly useful, as complaints of certain degrees may cause severe consequences for companies or organizations. We create the first Chinese dataset containing 3,103 posts about complaints from Weibo, a popular Chinese social media platform. These posts are then annotated with complaints intensity scores using Best-Worst Scaling (BWS) method. We show that complaints intensity can be accurately estimated by computational models with the best mean square error achieving 0.11. Furthermore, we conduct a comprehensive linguistic analysis around complaints, including the connections between complaints and sentiment, and a cross-lingual comparison for complaints expressions used by Chinese and English speakers. We finally show that our complaints intensity scores can be incorporated for better estimating the popularity of posts on social media.
