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Irony detection on microposts with limited set of features
Date
2017-04-04
Author
Taslioglu, Hande
Karagöz, Pınar
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Detecting irony in texts attracts computer scientists' attention as a recent research problem. Automatic detection of irony on microblog texts, i.e., microposts, poses additional challenges. Microposts have limited number of characters, and generally include typing errors, therefore traditional methods of text mining cannot be applied easily. This study aims to automatically detect irony in microposts. The proposed solution is based on supervised learning through a limited set of features extracted from the text. Experimental results show the effectiveness of the approach for Turkish and English informal texts.
Subject Keywords
Information systems
,
Information retrieval
,
Retrieval tasks and goals
,
Information extraction
,
Sentiment analysis
URI
https://hdl.handle.net/11511/44911
DOI
https://doi.org/10.1145/3019612.3019818
Collections
Department of Computer Engineering, Conference / Seminar
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H. Taslioglu and P. Karagöz, “Irony detection on microposts with limited set of features,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/44911.