Sarcasm Recognition on News Headlines Using Multiple Channel Embedding Attention BLSTM

Azika Syahputra Azwar, Suharjito Suharjito


Sarcasm is a statement that conveys an opposing viewpoint via positive or exaggeratedly positive phrases. Due to this intentional ambiguity, sarcasm identification has become one of the important factors in sentiment analysis that make many researchers in natural language processing intensively study sarcasm detection. This research is using multiple channels embedding the attention bidirectional long-short memory (MCEA-BLSTM) model that explored sarcasm detection in news headlines and has different approach from previous research-developed models that lexical, semantic, and pragmatic properties. This research found that multiple channels embedding attention mechanism improve the performance of BLSTM, making it superior to other models. The proposed method achieves 96.64% accuracy with an f-measure of 97%


Sarcasm detection;BLSTM;Attention;Multiple Channel;News Headline

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Katyayan, P., & Joshi, N. (2019). Sarcasm Detection approaches for the English language. In Smart Techniques for a Smarter Planet (pp. 167-183). Springer, Cham.

Pandey, A. C., Seth, S. R., & Varshney, M. (2019). Sarcasm detection of amazon Alexa sample set. In Advances in Signal Processing and Communication (pp. 559-564). Springer, Singapore.

Liu, L., Priestley, J. L., Zhou, Y., Ray, H. E., & Han, M. (2019). A2text-net: A novel deep neural network for sarcasm detection. In 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) (pp. 118-126). IEEE.

Peng, H., Li, J., He, Y., Liu, Y., Bao, M., Wang, L., & Yang, Q. (2018). Large-scale hierarchical text classification with recursively regularized deep graph-CNN. Proceedings of the 2018 World Wide Web Conference, (pp. 1063-1072).

Wang, Z., & Song, B. (2019). Research on hot news classification algorithm based on deep learning. In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (pp. 2376-2380). IEEE.

Mehndiratta, P., & Soni, D. (2019). Identification of sarcasm using word embeddings and hyperparameters tuning. Journal of Discrete Mathematical Sciences and Cryptography, 22(4), 465-489.

Cai, Y., Cai, H., & Wan, X. (2019, July). Multi-modal sarcasm detection in Twitter with hierarchical fusion model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 2506-2515).

Misra, R., & Arora, P. (2019). Sarcasm detection using a hybrid neural network. arXiv preprint arXiv:1908.07414.

Hiai, S., & Shimada, K. (2019). Sarcasm detection using run with relation vector. International Journal of Data Warehousing and Mining (IJDWM), 15(4), 66-78.

Kumar, A., Sangwan, S. R., Arora, A., Nayyar, A., & Abdel-Basset, M. (2019). Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE Access, 7, 23319-23328.

Xiong, T., Zhang, P., Zhu, H., & Yang, Y. (2019). Sarcasm detection with self-matching networks and low-rank bilinear pooling. In The World Wide Web Conference (pp. 2115-2124).

Jain, D., Kumar, A., & Garg, G. (2020). Sarcasm detection in mash-up language using soft-attention-based bi-directional LSTM and feature-rich CNN. Applied Soft Computing, 91, 106198.

Mandal, P. K., & Mahto, R. (2019). Deep CNN-LSTM with word embeddings for news headline sarcasm detection. In 16th International Conference on Information Technology-New Generations (ITNG 2019) (pp. 495-498). Springer, Cham.

Kumar, A., Narapareddy, V. T., Srikanth, V. A., Malapati, A., & Neti, L. B. M. (2020). Sarcasm detection using multi-head attention-based bidirectional LSTM. Ieee Access, 8, 6388-6397.

Onan, A. (2019). Topic-enriched word embeddings for sarcasm identification. In Computer Science On-line Conference (pp. 293-304). Springer, Cham.

Diao, Y., Lin, H., Yang, L., Fan, X., Chu, Y., Xu, K., & Wu, D. (2020). A multi-dimension question answering network for sarcasm detection. IEEE Access, 8, 135152-135161.

Salim, S. S., Ghanshyam, A. N., Ashok, D. M., Mazahir, D. B., & Thakare, B. S. (2020). Deep LSTM-RNN with word embedding for sarcasm detection on Twitter. In 2020 International Conference for Emerging Technology (INCET) (pp. 1-4). IEEE.

Sangwan, S., Akhtar, M. S., Behera, P., & Ekbal, A. (2020). I didn’t mean what I wrote! Exploring Multimodality for Sarcasm Detection. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.

Ashok, D. M., Ghanshyam, A. N., Salim, S. S., Mazahir, D. B., & Thakare, B. S. (2020). Sarcasm Detection using Genetic Optimization on LSTM with CNN. In 2020 International Conference for Emerging Technology (INCET) (pp. 1-4). IEEE.

Ren, L., Xu, B., Lin, H., Liu, X., & Yang, L. (2020). Sarcasm detection with sentiment semantics enhanced multi-level memory network. Neurocomputing, 401, 320-326.

Bedi, M., Kumar, S., Akhtar, M. S., & Chakraborty, T. (2021). Multi-modal sarcasm detection and humor classification in code-mixed conversations. IEEE Transactions on Affective Computing.

Kamal, A., & Abulaish, M. (2022). Cat-bigru: Convolution and attention with the bi-directional gated recurrent unit for self-deprecating sarcasm detection. Cognitive Computation, 14(1), 91-109.

Razali, M. S., Halin, A. A., Ye, L., Doraisamy, S., & Norowi, N. M. (2021). Sarcasm Detection Using Deep Learning With Contextual Features. IEEE Access, 9, 68609-68618.

Verma, P., Shukla, N., & Shukla, A. P. (2021). Techniques of sarcasm detection: A review. In 2021 International Conference on Advanced Computing and Innovative Technologies in Engineering (ICACITE) (pp. 968-972). IEEE.

Majumder, N., Poria, S., Peng, H., Chhaya, N., Cambria, E., & Gelbukh, A. (2019). Sentiment and sarcasm classification with multitask learning. IEEE Intelligent Systems, 34(3), 38-43.

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