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- W4285193724 abstract "In sentiment analysis, the most common attention and broad current research topic is irony/sarcastic/satire detection. The most complex problem is irony detection which is identifying the meaning or emotion of satire reviews. Sarcastic reviews have positive words but emotion is negative and vice versa. This research work has carried out for irony detection on Twitter Tweets of Amazon product’s reviews. Lexicon-based features with N-gram and Skip-gram-based methods are explored for irony detection. To recognize the best approach for irony detection and prediction, a total of 22,000 variegated irony and non-irony Tweets of Amazon products are collected and used with new deep learning (DL) approach. The results are compared with various machine learning approaches namely, decision tree (DT), support vector machine, logistic regression (LR), and random forest (RF). The proposed work is implemented to find irony detection. It is seen the proposed DL model has produced average results when compared to the classical machine learning DT and RF model." @default.
- W4285193724 created "2022-07-14" @default.
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- W4285193724 date "2022-01-01" @default.
- W4285193724 modified "2023-10-14" @default.
- W4285193724 title "Analysis of Approaches for Irony Detection in Tweets for Online Products" @default.
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- W4285193724 doi "https://doi.org/10.1007/978-981-19-0475-2_13" @default.
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