CRF-MEM: Conditional Random Field Model Based Modified Expectation Maximization Algorithm for Sarcasm Detection in Social Media,ERICDATA高等教育知識庫
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熱門: 羅文君  Yang Li  Yuanpeng Long  Xianyi Zhou  張國霖  林彥廷  
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篇名
CRF-MEM: Conditional Random Field Model Based Modified Expectation Maximization Algorithm for Sarcasm Detection in Social Media
並列篇名
CRF-MEM: Conditional Random Field Model Based Modified Expectation Maximization Algorithm for Sarcasm Detection in Social Media
作者 Anbarasu SivalingamKarthik SundararajanAnandhakumar Palanisamy
英文摘要

Text processing is an important task in various machine learning applications. One among the applications is Sentiment analysis. However, the presence of sarcasm makes it difficult for analyzing the sentiment of the statement. In the current scenario, the amount of sarcastic statements in any social media platform is high taking the forms of memes, comments, trolls etc. Hence it is important to identify sarcasm to preserve the polarity of any given statement. Sarcasm usually means the opposite of what the sentence seems to convey. While the existing works in literature have focused on detecting sarcasm, the proposed model, in addition to that, determines the levels of sarcasm present in the text, which will aid in finding the level of harshness present in the statement. In this work, an unsupervised learning model, Conditional Random Field model based Modified Expectation Maximization (CRF-MEM) algorithm has been proposed for detecting sarcasm in tweets. The proposed model aims to overcome the limitation present in the traditional EM algorithm, the random assignment factor, with the proposed aspect relationship value. Experimental results showed that the proposed CRF-MEM achieved an accuracy of 91.89% whereas the traditional EM displayed an accuracy of 80% in detecting sarcasm from text.

 

起訖頁 045-054
關鍵詞 Text miningNatural Language ProcessingComputational linguisticsArtificial intelligence
刊名 網際網路技術學刊  
期數 202301 (24:1期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642023012401005  複製DOI
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