Table 1.
Examples of sarcastic tweets.
Fig 1.
Overview of the proposed research methodology.
Table 2.
Inner annotator agreement distribution.
Table 3.
System configuration.
Table 4.
List of Urdu stop words.
Table 5.
Results of featured-based classifiers.
Fig 2.
Comparison of n-gram models.
Table 6.
Results analysis of ML textual classifiers.
Fig 3.
ROC curve showing the performance of ML classifiers.
Fig 4.
Learning curves to train the machine learning classifier.
Fig 5.
Confusion matrix generated for the ML classifier 4a for logistic regression and 4b for random forest.
Fig 6.
Performance comparison of classifiers on different datasets.
Table 7.
Tanz-Indicator model compared with the proposed model comparison.
Table 8.
Significance of our UST dataset in terms of T-Pair test.