lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Sentiment and Stance Visualization of Textual Data for Social Media
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS)ORCID iD: 0000-0002-1907-7820
2019 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Rapid progress in digital technologies has transformed the world in many ways during the past few decades, in particular, with the new means of communication such as social media. Social media platforms typically rely on textual data produced or shared by the users in multiple timestamped posts. Analyses of such data are challenging for traditional manual methods that are unable to scale up to the volume and the variety of the data. While computational methods can partially address these challenges, they have to be used together with the methods developed within information visualization and visual analytics to gain knowledge from the text data by using interactive visual representations.

One of the most interesting aspects of text data is related to expressions of sentiments and opinions. The corresponding task of sentiment analysis has been studied within computational linguistics, and sentiment visualization techniques exist as well. However, there are gaps in research on the related task of stance analysis, dedicated to subjectivity that is not expressible only in terms of sentiment. Research on stance is an area of interest in linguistics, but support by computational and visual methods has been limited so far. The challenges related to definition, analysis, and visualization of stance in textual data call for an interdisciplinary research effort. The StaViCTA project addressed these challenges with a focus on written text in English. The corresponding results in the area of visualization are reported in this work, based on multiple publications.

The main goal of this dissertation is to define, categorize, and implement means for visual analysis of sentiment and stance in textual data, in particular, for social media. Our work is based on the theoretical framework and automatic classifier of stance developed by our project collaborators, involving multiple non-exclusive stance categories such as certainty and prediction. We define a design space for sentiment and stance visualization techniques based on literature surveys. We discuss multiple visualization and visual analytics approaches developed by us to facilitate the underlying research on stance analysis, data collection and annotation, and visual analysis of sentiment and stance in real-world text data from several social media sources. The work described in this dissertation was carried out in cooperation with domain experts in linguistics and computational linguistics, and our approaches were validated with case studies, expert user reviews, and critical discussion. The results of this work open up further opportunities for research in text visualization and visual text analytics. The potential application areas are academic research, business intelligence, social media monitoring, and journalism.

Place, publisher, year, edition, pages
Växjö, Sweden: Linnaeus University Press, 2019. , p. 264
Series
Linnaeus University Dissertations ; 347/2019
Keywords [en]
stance visualization, sentiment visualization, text visualization, stance analysis, sentiment analysis, opinion mining, visualization, interaction, visual analytics, NLP, text mining, text analytics, social media
National Category
Computer Sciences Human Computer Interaction
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-81081ISBN: 978-91-88898-47-0 (print)ISBN: 978-91-88898-48-7 (electronic)OAI: oai:DiVA.org:lnu-81081DiVA, id: diva2:1296288
Public defence
2019-04-15, Wicksell, Hus K, Växjö, 09:15 (English)
Opponent
Supervisors
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Available from: 2019-03-19 Created: 2019-03-14 Last updated: 2019-03-19Bibliographically approved

Open Access in DiVA

kkucher-dissertation-2019(28092 kB)322 downloads
File information
File name FULLTEXT01.pdfFile size 28092 kBChecksum SHA-512
e78b174f7f089f0f71c4b989865d762696c7abb0069db7c059c46e1efdb0b8ec6448aa9d040515c84e3bb498b211a85aedd9ef0a02b6ce359864c00b62182498
Type fulltextMimetype application/pdf

Authority records BETA

Kucher, Kostiantyn

Search in DiVA

By author/editor
Kucher, Kostiantyn
By organisation
Department of computer science and media technology (CM)
Computer SciencesHuman Computer Interaction

Search outside of DiVA

GoogleGoogle Scholar
Total: 322 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 718 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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