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Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones(More)
What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model(More)
With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such(More)
This paper presents an approach for automating semantic annotation within service-oriented architectures that provide interfaces to databases of spatial-information objects. The automation of the annotation process facilitates the transition from the current state-of-the-art architectures towards semantically-enabled architectures. We see the annotation(More)
Studying the relationship between public sentiment and stock prices has been the focus of several studies. This paper analyzes whether the sentiment expressed in Twitter feeds, which discuss selected companies and their products, can indicate their stock price changes. To address this problem, an active learning approach was developed and applied to(More)
Many data mining techniques are these days in use for ontology learning – text mining, Web mining, graph mining, link analysis, relational data mining, and so on. In the current state-of-the-art bundle there is a lack of “software mining” techniques. This term denotes the process of extracting knowledge out of source code. In this paper we approach the(More)
Geospatial Web services allow to access and to process Geospatial data. Despite significant standardisation efforts, severe heterogeneity and interoperability problems remain. The SWING environment leverages the Semantic Web Services (SWS) paradigm to address these problems. The environment supports the entire life-cycle of Geospatial SWS. To this end, it(More)