In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand or organization. Opinion mining for product positioning, in fact, is getting a more and more popular research field but the extraction of useful information from social media is not a simple task. In this work we merge AI and Semantic Web techniques to extract, encode and represent this unstructured information. In particular, we use Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies. Eventually we represent this information as an interconnected knowledge base which is browsable through a multi-faceted classification website
The purpose of this paper is presenting a new advanced hardware/software system, boasting two main features: first it performs real time tracking of workers' routes in construction sites; then it implements an algorithm for preventing workers to be involved in hazardous situations. This research step is part of a wider ongoing research concerning the development of a new generation of advanced construction management systems, allowing for real-time monitoring and coordination of tasks, automatic health and safety management, on-site delivering of technical information, capture of as-built documentation. Exploiting the high accuracy provided by the UWB system responsible for position tracking and successfully tested in previous research, our software interface is able to graphically reproduce (and store) the travel patterns of workers. Moreover, it constantly checks if they are accessing hazardous areas, using an algorithm based on a predictive approach: it is conceived to predict in advance whether any worker is approaching a forbidden area, in fact performing virtual fencing. This approach could be easily extended to other applications, too. Some preliminary tests simulated in the DACS laboratory are described and the obtained results discussed.
The recent success of media-sharing services caused an exponential growth of community-contributed multimedia data on the Web and hence a consistent shift of the flow of information from traditional communication channels to social media ones. Retrieving relevant information from this kind of data is getting more and more difficult, not only for their volume, but also for the different nature and formats of their contents. In this work, we introduce Sentic Web, a new paradigm for the management of social media affective information, which exploits AI and Semantic Web techniques to extract, encode, and represent opinions and sentiments over the Web. In particular, the computational layer consists in an intelligent engine for the inference of emotions from text, the representation layer is developed on the base of specific domain ontologies, and the application layer is based on the faceted browsing paradigm to make contents available as an interconnected knowledge base
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