Abstract
Data science applications often need to deal with data that does not fit into the standard entity-attribute-value model. In this chapter we discuss three of these other types of data. We discuss texts, images and graphs. The importance of social media is one of the reason for the interest on graphs as they are a way to represent social networks and, in general, any type of interaction between people. In this chapter we present examples of tools that can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.
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Bae, J., Karlsson, A., Mellin, J., Ståhl, N., Torra, V. (2019). Complex Data Analysis. In: Said, A., Torra, V. (eds) Data Science in Practice. Studies in Big Data, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-97556-6_9
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