Abstract
Today Twitter is one of the most important sources for information distribution. But finding useful and interesting tweets on a specific topic is a non-trivial task, because there are thousands of new posts every minute. In this paper, we describe our preference-based search approach on Twitter messages, which allows users to get the best possible results. For this, we introduce a new CONTAINS preference constructor to search on full-text data, use NLP techniques to handle natural language mistakes, and present experiments.
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Rudenko, L., Haas, C., Endres, M. (2020). Analyzing Twitter Data with Preferences. In: Darmont, J., Novikov, B., Wrembel, R. (eds) New Trends in Databases and Information Systems. ADBIS 2020. Communications in Computer and Information Science, vol 1259. Springer, Cham. https://doi.org/10.1007/978-3-030-54623-6_16
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DOI: https://doi.org/10.1007/978-3-030-54623-6_16
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