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Topic-Modeling- and Subject-Classification-Analyses of Articles from the EURASIP Journal on Advances in Signal Processing
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This data set contains the results of topic-modeling- and subject-classification-analyses of the abstracts of 87 articles from the EURASIP Journal on Advances in Signal Processing (ISSN: 1687-6180). All of the selected articles had in common that they were assigned the keyword “OFDM” (Orthogonal Frequency-Division Multiplexing) by the authors or the publisher.
The topic modeling analyses were carried out with the program GibbsLDA++ (http://gibbslda.sourceforge.net) once with and once without stemming (model-final.twords_w_stemming.txt and model-final.twords_wo_stemming.txt, respectively). The program parameters were set to: src/lda -est -alpha 0.5 -beta 0.1 -ntopics 10 -niters 1000 -savestep 100 -twords 20
The subject classification analyses were carried out with the web-application Annif.org (http://annif.org/), which offers different algorithms for the classification. The following algorithms were used (the name of the corresponding result file is given in brackets): Annif prototype API English (Annif.png), fastText English (fastText.png), Maui English (Maui.png), TF-IDF English (TF-IDF.png), YSO ensemble English (YSO.png).
A list with the DOIs of the articles can be found in the file "DOIs_analyzed_articles.txt" and the analyzed abstracts of these articles in the zip archive "Abstracts_EURASIPJAdvSignalProcess.zip".
SubjectTDM;Text and Data Mining;Topic Modeling;Subject Classification;OFDM;Orthogonal Frequency-Division Multiplexing;Orthogonales Frequenzmultiplexverfahren
DFG subject classification104-04 Angewandte Sprachwissenschaften, Experimentelle Linguistik, Computerlinguistik
408-02 Nachrichten- und Hochfrequenztechnik, Kommunikationstechnik und -netze, Theoretische Elektrotechnik
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