La persona habla sobre su familia que es de Culiacan, Sinaloa, México. Brevemente saluda preguntando cómo están y menciona que su familia es originaria de Culiacan.
How to Setup your LinkedIn Company PresenceJenson Tham
As LinkedIn becomes the definitive platform for professional content and information on your company, jobs, products/services, and more; people are coming to your LinkedIn Company Page whether it's active or not. Learn the components of a Company Page on LinkedIn - how to setup the right profile, attract an audience, and with the right foundation extend your company presence with the right tools (Career Pages, Showcase Pages, Groups, Slideshare).
http://arxiv.org/abs/1511.00722
Text actionability detection is the problem of classifying user authored natural language text, according to whether it can be acted upon by a responding agent. In this paper, we propose a supervised learning framework for domain-aware, large-scale actionability classification of social media messages. We derive lexicons, perform an in-depth analysis for over 25 text based features, and explore strategies to handle domains that have limited training data. We apply these methods to over 46 million messages spanning 75 companies and 35 languages, from both Facebook and Twitter. The models achieve an aggregate population-weighted F measure of 0.78 and accuracy of 0.74, with values of over 0.9 in some cases.
How to Setup your LinkedIn Company PresenceJenson Tham
As LinkedIn becomes the definitive platform for professional content and information on your company, jobs, products/services, and more; people are coming to your LinkedIn Company Page whether it's active or not. Learn the components of a Company Page on LinkedIn - how to setup the right profile, attract an audience, and with the right foundation extend your company presence with the right tools (Career Pages, Showcase Pages, Groups, Slideshare).
http://arxiv.org/abs/1511.00722
Text actionability detection is the problem of classifying user authored natural language text, according to whether it can be acted upon by a responding agent. In this paper, we propose a supervised learning framework for domain-aware, large-scale actionability classification of social media messages. We derive lexicons, perform an in-depth analysis for over 25 text based features, and explore strategies to handle domains that have limited training data. We apply these methods to over 46 million messages spanning 75 companies and 35 languages, from both Facebook and Twitter. The models achieve an aggregate population-weighted F measure of 0.78 and accuracy of 0.74, with values of over 0.9 in some cases.