|Authors||A. Nordskog, P. Halvorsen, S. Hicks, H. K. Stensland, H. L. Hammer, D. Johansen and M. Riegler|
|Title||Semantic Analysis of Soccer News for Automatic Game Event Classification|
|Project(s)||Simula Metropolitan Center for Digital Engineering|
|Publication Type||Proceedings, refereed|
|Year of Publication||2019|
|Conference Name||Content-Based Multimedia Indexing (CBMI)|
We are today overwhelmed with information, of which an important part is news. Sports news, in particular, has become very popular, where soccer makes up a big part of this coverage. For sports fans, it can be a time consuming and tedious to keep up with the news that they really care about. In this paper, we present different machine learning methods applied to soccer news from a Norwegian newspaper and a TV station's news site to summarize the content in a short and digestible manner. We present a system to collect, index, label, analyze, and present the collected news articles based on the content. We perform a thorough comparison between deep learning and traditional machine learning algorithms on text classification. Furthermore, we present a dataset of soccer news which was collected from two different Norwegian news sites and shared online.