About: Clustering a very large number of textual unstructured customers' reviews in English

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Clustering a very large number of textual unstructured customers' reviews in English Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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. (en)
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 (en)
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 (en)
skos:notation	RIV/62156489:43110/12:00215791!RIV14-MSM-43110___
http://linked.open...avai/riv/aktivita	Z
http://linked.open...avai/riv/aktivity	Z(MSM6215648904)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Dařena, František Žižka, Jan Burda, Karel
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Mendelova univerzita v Brně / Provozně ekonomická fakulta
http://linked.open...dnocenehoVysledku	127488
http://linked.open...ai/riv/idVysledku	RIV/62156489:43110/12:00215791
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	similarity; clustering criterion function; cluster mining; entropy; textual data; term representation; customer opinion (en)
http://linked.open.../riv/klicoveSlovo	entropy textual data similarity cluster mining clustering criterion function customer opinion term representation
http://linked.open...ontrolniKodProRIV	[68186B10EC84]
http://linked.open...v/mistoKonaniAkce	Varna
http://linked.open...i/riv/mistoVydani	Heidelberg Dordrecht London New York
http://linked.open...i/riv/nazevZdroje	Artificial Intelligence: Methodology, Systems, and Applications
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...UplatneniVysledku	2012
http://linked.open...iv/tvurceVysledku	Burda, Karel Žižka, Jan Dařena, František
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2012-09-12 (xsd:date)
http://linked.open...n/vavai/riv/zamer	The Czech Economy in the Process of Integration and Globalisation, and the Development of Agricultural Sector and the Sector of Services under the New Conditions of the Integrated European Market
number of pages	10 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-642-33185-5_5
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-642-33184-8
http://localhost/t...ganizacniJednotka	43110

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 81 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software