有限特征微博的反讽检测

Hande Taslioglu, P. Senkul
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引用次数: 5

摘要

文本反讽的检测作为一个新的研究课题引起了计算机科学家的关注。自动检测微博文本(即微博)上的反讽也带来了额外的挑战。微博的字符数量有限,并且通常包含输入错误,因此传统的文本挖掘方法不容易应用。本研究旨在自动检测微博中的反讽。提出的解决方案是基于监督学习,通过从文本中提取的有限特征集。实验结果表明了该方法对土耳其语和英语非正式文本的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Irony detection on microposts with limited set of features
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.
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