Understanding Discourse on Work and Job-Related Well-Being in Public Social Media
نویسندگان
چکیده
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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ورودعنوان ژورنال:
- Proceedings of the conference. Association for Computational Linguistics. Meeting
دوره 2016 شماره
صفحات -
تاریخ انتشار 2016