Unfolding Sarcasm in Twitter Using C-RNN Approach





Sarcasm Detection, Deep Learning, CNN, RNN, CNN-LSTM, Twitter


Sarcasm detection in text is an inspiring field to explore due to its contradictory behavior. Textual data can be analyzed in order to discover clues those lead to sarcasm. A Deep learning-based framework is applied in this paper in order to extract sarcastic clues automatically from text data. In this context, twitter news dataset is exploited to recognize sarcasm. Convolutional-Recurrent Neural network (C-RNN) based model is proposed in this paper that enables automatic discovery of sarcastic pattern detection. The proposed model consists of two major layers such as convolutional layer, and Long-term short memory (LSTM) layers. LSTM is known to be a variant of traditional RNN. Experimental results confirmed sarcastic news detection with promising accuracy of 84.73%. This research work exhibits its uniqueness in combining two dissimilar Deep Learning frameworks under a single entity for predicting sarcastic posts.


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M. Bouazizi and T. Otsuki, "A Pattern-Based Approach for Sarcasm Detection on Twitter," IEEE Access, vol. 4, pp. 5477-5488, 2016, doi: 10.1109/ACCESS.2016.2594194.

R. Giora, O. Fein, J. Ganzi, N. A. Levi, and H. Sabah, "On negation as mitigation: The case of negative irony," Discourse Process., vol. 39, no. 1, pp. 81-100, 2005, doi: 10.1207/s15326950dp3901_3.

S. L. Ivanko and P. M. Pexman, "Context Incongruity and Irony Processing Context Incongruity and Irony Processing," no. 918551878, 2010, doi: 10.1207/S15326950DP3503.

J. Schmidhuber, "Deep Learning in neural networks: An overview," Neural Networks, vol. 61, pp. 85-117, 2015, doi: 10.1016/j.neunet.2014.09.003.

M. Tom, "Recurrent neural network based language model ' s Mikolov Introduction Comparison and model combination Future work," pp. 1-24, 2010.

H. C. Shin et al., "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning," IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285-1298, 2016, doi: 10.1109/TMI.2016.2528162.

Z. Huang, W. Xu, and K. Yu, "Bidirectional LSTM-CRF Models for Sequence Tagging," 2015.

S. Lukin and M. Walker, "Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue," vol. 1, 2017.

S. Amir, B. C. Wallace, H. Lyu, P. Carvalho, and M. J. Silva, "Modelling context with user embeddings for sarcasm detection in social media," CoNLL 2016 - 20th SIGNLL Conf. Comput. Nat. Lang. Learn. Proc., pp. 167-177, 2016, doi: 10.18653/v1/k16-1017.

R. González-ibáñez and N. Wacholder, "Identifying Sarcasm in Twitter?: A Closer Look," no. 2010, pp. 581-586, 2011.

C. Chang and C. Lin, "LIBSVM?: A Library for Support Vector Machines," vol. 2, no. 3, 2011, doi: 10.1145/1961189.1961199.

R. E. Wright, "Logistic regression.," in Reading and understanding multivariate statistics., Washington, DC, US: American Psychological Association, 1995, pp. 217-244.

F. Barbieri, H. Saggion, and F. Ronzano, "Modelling Sarcasm in Twitter , a Novel Approach," pp. 50-58, 2014. https://doi.org/10.3115/v1/W14-2609

Quinlan J.R, "Simplifying Decision Trees," International Journal of Man-Machine Studies, vol. 27, no. 3. pp. 221-234, 1987. https://doi.org/10.1016/S0020-7373(87)80053-6

Wang, Z and Y. R. Zelin Wang, Zhijian Wu, Ruimin Wang, "Twitter Sarcasm Detection Exploiting a Context-Based Model," Lect. Notes Comput. Sci., pp. 77-91, 2015. https://doi.org/10.1007/978-3-319-26190-4_6

H. M. Wallach, "Topic Modeling?: Beyond Bag-of-Words," no. 1, pp. 977-984, 2006. https://doi.org/10.1145/1143844.1143967

A. Joshi, V. Sharma, and P. Bhattacharyya, "Harnessing context incongruity for sarcasm detection," ACL-IJCNLP 2015 - 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Int. Jt. Conf. Nat. Lang. Process. Asian Fed. Nat. Lang. Process. Proc. Conf., vol. 2, no. 2003, pp. 757-762, 2015, doi: 10.3115/v1/p15-2124.

A. Ghosh and D. T. Veale, "Fracking Sarcasm using Neural Network," pp. 161-169, 2016, doi: 10.18653/v1/w16-0425.

W. Liu, Y. Wen, Z. Yu, and M. Yang, "Large-Margin Softmax Loss for Convolutional Neural Networks," 2016.

Rishabh Misra (October,2018), "News Headlines Dataset For Sarcasm Detection" Version 2. Retrieved on 24.05.2020 available from https://www.kaggle.com/rmisra/news-headlines-dataset-for-sarcasm-detection.

Y. Li and Y. Yuan, "Convergence analysis of two-layer neural networks with RELU activation," Adv. Neural Inf. Process. Syst., vol. 2017-Decem, no. Nips, pp. 598-608, 2017.

M. R. Zadeh, S. Amin, D. Khalili, and V. P. Singh, "Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions," Water Resour. Manag., vol. 24, no. 11, pp. 2673-2688, 2010, doi: 10.1007/s11269-009-9573-4.

"Keras." [Online]. Available: https://keras.io.

D. P. Kingma and J. L. Ba, "Adam: A method for stochastic optimization," 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1-15.

E. Sutoyo and A. Almaarif, "Twitter Sentiment Analysis of The Relocation of Indonesia's Capital City," Bull. Electr. Eng. Informatics, vol. 9, no. 04, pp. 1620-1630, 2020, doi: 10.11591/eei.v9i4.2352.

R. Novendri, A. S. . Callista, D. N. Pratama, and C. E. . Puspita, "Sentiment Analysis of YouTube Movie Trailer Comments Using Naïve Bayes", Bulletin of Comp. Sci. Electr. Eng., vol. 1, no. 1, pp. 26-32, Jun. 2020.

R. Permatasari and N. A. Rakhmawati, "Features Selection for Entity Resolution in Prostitution on Twitter", Int. J. Adv. Data Inf. Syst., vol. 2, no. 1, pp. 53-61, Mar. 2021. https://doi.org/10.25008/ijadis.v2i1.1214




How to Cite

Dutta, S., & Akash Mehta. (2021). Unfolding Sarcasm in Twitter Using C-RNN Approach. Bulletin of Computer Science and Electrical Engineering, 2(1), 1–8. https://doi.org/10.25008/bcsee.v2i1.1134