Irony detection on microposts with limited set of features

Date

2017-04-04

Author

Taslioglu, Hande
Karagöz, Pınar

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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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Citation Formats

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.