Semantic knowledge inference from online news media using an LDA-NLP approach

The amount of news delivered by the different media in the current environment can be overwhelming. Although the events being reported are factually the same, the ways with which the news is delivered vary with the media sources involved. In many cases, it is difficult to reliably uncover the latent information hidden within the news reports due to the great diversity of topics and the sheer volume of news. Analysis of the news media has always been of interest to news analysts, politicians and policy makers in order to aggregate and make sense of the information generated every day. News sources try to achieve relevance to their audiences by providing them with news that the audience wants or finds interesting, but often also have implicit motives such as shaping the perceptions of their audience. Although these agendas or target audiences are not explicitly identified, we consider ways in which this information can be inferred by applying the tools of natural language processing and semantic analysis to the news streams from these sources.

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