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Sensing the online social sphere using a sentiment analytical approach
University of Ravensburg-Weingarten. (ETOUR)
Mid Sweden University, Faculty of Human Sciences, Department of Tourism Studies and Geography. (ETOUR)ORCID iD: 0000-0003-3964-2716
University of Ravensburg-Weingarten. (ETOUR)
Mid Sweden University, Faculty of Human Sciences, Department of Tourism Studies and Geography. (ETOUR)ORCID iD: 0000-0002-6610-9303
2016 (English)In: Analytics in smart tourism design: Concepts and methodologies / [ed] Z. Xiang and D. R. Fesenmaier, Springer, 2016, 129-146 p.Chapter in book (Refereed)
Abstract [en]

Customer online feedback in the form of user-generated content (UGC) has become one of the most contentful and influential source of information in the process of customers’ as well as suppliers’ decision making. Thus, extracting customer feedback from online platforms and detecting its sentiment as well as related topics, known as sentiment analysis or opinion mining, not surprisingly, became one of the most important and vivid research veins within the area of web mining. This chapter gives an overview of different approaches to tackle the problem of sentiment analysis, like simple word-list-based approaches or more complex machine learning approaches, making use of statistical language models or part-of-speech (POS) tagging, and discusses current applications in the field of tourism. Subsequently, the chapter describes selected sentiment analytical approaches in more detail. Sentiment detection is tackled by simple word-list-based approaches and by typical supervised learning approaches, like k-nearest neighbor, support vector machines and Naive Bayes. Additionally to these approaches, topic detection is tackled by methods of unsupervised learning, like cluster analysis and single value decomposition. All presented techniques are demonstrated and validated based on a prototypical implementation as part of a destination management information system (DMISTM) for the leading Swedish mountain destination Åre.

Place, publisher, year, edition, pages
Springer, 2016. 129-146 p.
Keyword [en]
Sentiment analysis; opinion mining; UGC; Knowledge management; Tourism
National Category
Economic Geography
Identifiers
URN: urn:nbn:se:miun:diva-29231DOI: 10.1007/978-3-319-44263-1Local ID: ETOURISBN: 978-3-319-44263-1 (print)OAI: oai:DiVA.org:miun-29231DiVA: diva2:1045148
Projects
Kunskapsdestinationen II
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2017-01-13Bibliographically approved

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Fuchs, MatthiasLexhagen, Maria
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf