his.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
The Business Value of Text Mining
Högskolan i Skövde, Institutionen för informationsteknologi.
2017 (engelsk)Independent thesis Basic level (degree of Bachelor), 20 poäng / 30 hpOppgave
Abstract [en]

Text mining is an enabling technology that will come to change the process for how businesses derive insights & knowledge from the textual data available to them. The current literature has its focus set on the text mining algorithms and techniques, whereas the practical aspects of text mining are lacking. The efforts of this study aims at helping companies understand what the business value of text mining is with the help of a case study. Subsequently, an SMS-survey method was used to identify additional business areas where text mining could be used to derive business value from. A literature review was conducted to conceptualize the business value of text mining, thus a concept matrix was established. Here a business category and its relative: derived insights & knowledge, domain, and data source are specified. The concept matrix was from then on used to decide when information was of business value, to prove that text mining could be used to derive information of business value.Text mining analyses was conducted on traffic school data of survey feedback. The results were several patterns, where the business value was derived mainly for the categories of Quality Control & Quality Assurance. After comparing the results of the SMS-survey with the case study empiricism, some difficulties emerged in the categorization of derived information, implying the categories are required to become more specific and distinct. Furthermore, the concept matrix does not comprise all of the business categories that are sure to exist.

sted, utgiver, år, opplag, sider
2017. , s. 64
Emneord [en]
business value, text mining, survey data analysis, business value of text mining
HSV kategori
Identifikatorer
URN: urn:nbn:se:his:diva-13740OAI: oai:DiVA.org:his-13740DiVA, id: diva2:1110823
Eksternt samarbeid
IP.1
Fag / kurs
Informationsteknologi
Utdanningsprogram
Information Systems - Business Intelligence
Veileder
Examiner
Tilgjengelig fra: 2017-06-16 Laget: 2017-06-16 Sist oppdatert: 2017-06-16bibliografisk kontrollert

Open Access i DiVA

BVTM(1396 kB)734 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1396 kBChecksum SHA-512
c5404d546312c37fe025713496389f3fb758f6cd0718457ed04bdc010b63ab665d84db1c16299247c5128e40b66a6d3cf76f0673d96a5eddac1291d111935963
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 734 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 1022 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf