Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data Luber, Mattias Weisser, Christoph Säfken, Benjamin Silbersdorff, Alexander Kneib, Thomas Kis-Katos, Krisztina Bright, Jonathan Giachanou, Anastasia Spaiser, Viktoria Spezzano, Francesca George, Anna Pavliuc, Alexandra Springer 2021 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://www.hsbi.de/publikationsserver/record/6951 Luber M, Weisser C, Säfken B, Silbersdorff A, Kneib T, Kis-Katos K. Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data. In: Bright J, Giachanou A, Spaiser V, Spezzano F, George A, Pavliuc A, eds. <i>Disinformation in Open Online Media. MISDOOM 2021</i>. Lecture Notes in Computer Science, Vol. 12887. Cham: Springer; 2021:33-49. doi:<a href="https://doi.org/10.1007/978-3-030-87031-7_3">10.1007/978-3-030-87031-7_3</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-87031-7_3 info:eu-repo/semantics/altIdentifier/isbn/978-3-030-87030-0 info:eu-repo/semantics/closedAccess