Visual Analysis of Humor Assessment Annotations for News Headlines in the Humicroedit Data Set

Simple item page
dc.contributor.authorKucher, Kostiantynen_US
dc.contributor.authorAkkurt, Elinen_US
dc.contributor.authorFolde, Johannaen_US
dc.contributor.authorKerren, Andreasen_US
dc.contributor.editorYousef, Tariqen_US
dc.contributor.editorAl-Khatib, Khaliden_US
dc.date.accessioned2024-05-21T09:07:55Z
dc.date.available2024-05-21T09:07:55Z
dc.date.issued2024
dc.description.abstractEffective utilization of training data is a critical component for the success of any artificial intelligence algorithm, including natural language processing (NLP) tasks. One particular task of interest is related to detecting or ranking humor in texts, as exemplified by the Humicroedit data set used for the SemEval 2020 task of assessing humor in micro-edited news headlines. Rather than focusing on text classification or prediction, in this study, we focus on gaining a deeper understanding and utilization of the data through the use of information visualization techniques facilitated by the established NLP methods such as sentiment analysis and topic modeling. We describe the design of an interactive visualization tool prototype that relies on multiple coordinated views to allow the user explore and analyze the relationships between the annotated humor scores, sentiments, and topics. Evaluation of the proposed approach involves a case study with the Humicroedit data set as well as domain expert reviews with four participants. The experts deemed the prototype useful for its purpose and saw potential in exploring similar data sets with it, as well as further potential applications in their line of work. Our study thus contributes to the body of work on visual text analytics for supporting computational humor analysis as well as annotated text data analysis in general.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationVis4NLP 2024 - Workshop on Visualization for Natural Language Processing
dc.identifier.doi10.2312/vis4nlp.20241134
dc.identifier.isbn978-3-03868-259-2
dc.identifier.pages7 pages
dc.identifier.urihttps://doi.org/10.2312/vis4nlp.20241134
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/vis4nlp20241134
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visual analytics; Information visualization; Computing methodologies → Natural language processing
dc.subjectHuman centered computing → Visual analytics
dc.subjectInformation visualization
dc.subjectComputing methodologies → Natural language processing
dc.titleVisual Analysis of Humor Assessment Annotations for News Headlines in the Humicroedit Data Seten_US
Files
Original bundle
Now showing 1 - 3 of 3
No Thumbnail Available
Name:
02_vis4nlp20241134.pdf
Size:
1.12 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
1006-file-i7.mp4
Size:
19.93 MB
Format:
Video MP4
Download
No Thumbnail Available
Name:
1006-file-i8.vtt
Size:
8.27 KB
Format:
Web Video Text Tracks Format
Download
Collections
Vis4NLP2024