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Statements

Subject Item
n2:RIV%2F62156489%3A43110%2F12%3A00215791%21RIV14-MSM-43110___
rdf:type
skos:Concept n5:Vysledek
dcterms:description
Having a very large volume of unstructured text documents representing different opinions without knowing which document belongs to a certain category, clustering can help reveal the classes. The presented research dealt with almost two millions of opinions concerning customers' (dis)satisfaction with hotel services all over the world. The experiments investigated the automatic building of clusters representing positive and negative opinions. For the given high-dimensional sparse data, the aim was to find a clustering algorithm with a set of its best parameters, similarity and clustering-criterion function, word representation, and the role of stemming. As the given data had the information of belonging to the positive or negative class at its disposal, it was possible to verify the efficiency of various algorithms and parameters. From the entropy viewpoint, the best results were obtained with k-means using the binary representation with the cosine similarity, idf, and H2 criterion function, while stemming played no role. Having a very large volume of unstructured text documents representing different opinions without knowing which document belongs to a certain category, clustering can help reveal the classes. The presented research dealt with almost two millions of opinions concerning customers' (dis)satisfaction with hotel services all over the world. The experiments investigated the automatic building of clusters representing positive and negative opinions. For the given high-dimensional sparse data, the aim was to find a clustering algorithm with a set of its best parameters, similarity and clustering-criterion function, word representation, and the role of stemming. As the given data had the information of belonging to the positive or negative class at its disposal, it was possible to verify the efficiency of various algorithms and parameters. From the entropy viewpoint, the best results were obtained with k-means using the binary representation with the cosine similarity, idf, and H2 criterion function, while stemming played no role.
dcterms:title
Clustering a very large number of textual unstructured customers' reviews in English Clustering a very large number of textual unstructured customers' reviews in English
skos:prefLabel
Clustering a very large number of textual unstructured customers' reviews in English Clustering a very large number of textual unstructured customers' reviews in English
skos:notation
RIV/62156489:43110/12:00215791!RIV14-MSM-43110___
n5:predkladatel
n6:orjk%3A43110
n3:aktivita
n9:Z
n3:aktivity
Z(MSM6215648904)
n3:dodaniDat
n12:2014
n3:domaciTvurceVysledku
n10:9299483 n10:3970663 n10:6208894
n3:druhVysledku
n16:D
n3:duvernostUdaju
n23:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
127488
n3:idVysledku
RIV/62156489:43110/12:00215791
n3:jazykVysledku
n18:eng
n3:klicovaSlova
similarity; clustering criterion function; cluster mining; entropy; textual data; term representation; customer opinion
n3:klicoveSlovo
n13:customer%20opinion n13:textual%20data n13:entropy n13:cluster%20mining n13:clustering%20criterion%20function n13:term%20representation n13:similarity
n3:kontrolniKodProRIV
[68186B10EC84]
n3:mistoKonaniAkce
Varna
n3:mistoVydani
Heidelberg Dordrecht London New York
n3:nazevZdroje
Artificial Intelligence: Methodology, Systems, and Applications
n3:obor
n22:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n12:2012
n3:tvurceVysledku
Dařena, František Žižka, Jan Burda, Karel
n3:typAkce
n14:WRD
n3:zahajeniAkce
2012-09-12+02:00
n3:zamer
n11:MSM6215648904
s:numberOfPages
10
n19:doi
10.1007/978-3-642-33185-5_5
n17:hasPublisher
Springer-Verlag
n21:isbn
978-3-642-33184-8
n4:organizacniJednotka
43110