Sarcasm Detection: A Comparative Analysis of RoBERTa-CNN vs RoBERTa-RNNArchit ectures

Pawestri, Sheraton and Murinto, Murinto and Auzan, Muhammad (2024) Sarcasm Detection: A Comparative Analysis of RoBERTa-CNN vs RoBERTa-RNNArchit ectures. [Artikel Dosen]

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Abstract

Increasingly advanced technology and the creation of social media and
the internet can become a forum for people to express things or
opinions. However, comments or views from users sometimes contain
sarcasm making it more difficult to understand. News headlines,
sometimes contain sarcasm which makes readers confused about the
content of the news. Therefore, in this research, a model was created for
sarcasm detection. Many methods are used for sarcasm detection, but
performance still needs to be improved. So this research aims to
compare the performance of two text classification methods,
Convolutional Neural Network (CNN) and Recurrent Neural Network
(RNN), in detecting sarcasm in English news headlines using RoBERTa
text transformation. RoBERTa produces a fixed-size vector of numbers
1x768. The research results show that CNN has better performance than
RNN. CNN achieved the highest average accuracy of 0.891, precision
of 0.878, recall of 0.874, and f1-score of 0.876, with a loss of 0.260 and
a processing time of 508.1 milliseconds per epoch. But overall highest
can reach in fold 6, 0.897 for validation accuracy, 0.883 for F1-score
and precision, and 0.882 for recall in CNN + RoBERTa model. On the
contrary, RNN shows an accuracy of 0.711, precision of 0.692, recall
of 0.620, f1-score 0.654, and loss of 0.564, with a longer processing
time of 116500 milliseconds per epoch. The 10-fold cross-validation evaluation method ensures the model performs well and avoids overfitting. So, it is recommended to use the combination of RoBERTa and CNN in other text classification applications that require high speed and accuracy. Further research is recommended to explore deeper CNN architectures or other architectural variations, such as Transformer
models for performance improvements.

Item Type:	Artikel Dosen
Subjects:	A General Works > AC Collections. Series. Collected works
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Informatics Engineering (S1-Teknik Informatika)
Depositing User:	murinto murinto
Date Deposited:	30 Jan 2025 01:29
Last Modified:	30 Jan 2025 01:29
URI:	http://eprints.uad.ac.id/id/eprint/79027

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