Facilitating Twitter data analytics

Platform, language and functionality

Conference paper (2015)

Authors

K. Tao Web Information Systems - EEMCS
C. Hauff Web Information Systems - EEMCS
G.J.P.M. Houben Web Information Systems - EEMCS
Fabian Abel XING AG
G.H. Wachsmuth Web Information Systems - EEMCS , Programming Languages - EEMCS
Research Group
Web Information Systems (EEMCS) (TU Delft)
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Published Date

07-01-2015

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

Abstract

Conducting analytics over data generated by Social Web portals such as Twitter is challenging, due to the volume, variety and velocity of the data. Commonly, adhoc pipelines are used that solve a particular use case. In this paper, we generalize across a range of typical Twitter-data use cases and determine a set of common characteristics. Based on this investigation, we present our Twitter Analytical Platform (TAP), a generic platform for conducting analytical tasks with Twitter data. The platform provides a domain-specific Twitter Analysis Language (TAL) as the interface to its functionality stack. TAL includes a set of analysis tools ranging from data collection and semantic enrichment, to machine learning. With these tools, it becomes possible to create and customize analytical workflows in TAL and build applications that make use of the analytics results. We showcase the applicability of our platform by building Twinder-a search engine for Twitter streams.