理解公共社交媒体中关于工作和工作相关幸福感的话语

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2016-08-01 DOI:10.18653/v1/P16-1099

Tong Liu, Christopher Homan, Cecilia Ovesdotter Alm, Megan C. Lytle-Flint, Ann Marie White, Henry A. Kautz

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引用次数: 17

摘要

我们构建了一个人在循环监督学习框架，该框架集成了众包反馈和本地知识，以检测来自个人和企业账户的与工作相关的推文。使用数据驱动的人种学，我们通过将基于语言的分析与时间、地理和劳工统计信息融合在一起来研究关于工作的话语。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Understanding Discourse on Work and Job-Related Well-Being in Public Social Media

We construct a humans-in-the-loop supervised learning framework that integrates crowdsourcing feedback and local knowledge to detect job-related tweets from individual and business accounts. Using data-driven ethnography, we examine discourse about work by fusing language-based analysis with temporal, geospational, and labor statistics information.

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Proceedings of the conference. Association for Computational Linguistics. Meeting

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