| (Gupta et al., 2020) |
Tweets |
Combined the punctuation and sentiment related features with top 200 features extracted by TF-IDF for a voting classifier |
Extraction and elimination of punctuation and sarcastic features enhances the accuracy of sarcasm detection. |
| (Kumar & Garg, 2019) |
Typo-graphic Memes |
Incorporated semantic, lexical, and pragmatic features with KNN, decision tree, support vector classifier (SVC) with RBF kernel and linear kernel, random forest (RF), and multiLayer perceptron (MLP). |
Hand-crafted features help to enhance the performance of the MLP with typo-graphic memes. |
| (Khatri, Pranav & Anand, 2020) |
Tweets |
GloVe and BERT embedding with logistic regression, SVM, RF, and Gaussian Naïve (GN). |
Efficiency of sarcasm detection is elevated by incorporating embedding. |
| (Lemmens et al., 2020) |
1. Tweets 2. Reddit Comments |
Ensemble of adaboost classifier integrated with decision tree as base estimator, learning probabilities of sarcasm predicted by four component models including LSTM, MLP, CNN- LSTM , and SVM. |
Sarcasm detection on Reddit data is intrinsically more challenging. |
| (Kumar et al., 2020) |
Reddit Comments |
Bidirectional Long Short-Term Memory integrated with multi-head attention (MHA-BiLSTM). |
Incorporating multi-head attention-based system in BiLSTM improves the sarcasm detection accuracy. |
| (Javdan, Minaei-Bidgoli & Atai, 2020) |
1. Tweets 2. Reddit Comments |
Several models including NBSVM, BERT, BERT-SVM, BERT-LR, XLNET, Bi-GRU-CNN+BiLSTM-CNN, IAN, LCF-BERT, and BERT-AEN |
Models pre-trained with a combination of BERT and aspect-based sentiment analysis enhances the performance of sarcsdm detection. |
| (Son et al., 2019) |
Tweets |
A hybrid of soft attention-based LSTM and CNN |
Semantic word embeddings from GloVe assists helps to show robustness for sarcasm detection. |
| (Jena, Sinha & Agarwal, 2020) |
1. Tweets 2. Reddit Comments |
Contextual-Network (C-Net) |
Sarcastic nature of a conversation can be efficiently captured by integrating the context. |
| (Majumder et al., 2019) |
Text Snippets |
GRU-based neural network |
There is a correlation between sentiment and sarcasm of the context. |
