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Amit Khan


Sarcasm is a means of conveying a bad attitude through social media platforms by utilizing positive or exaggerated positive terms. The last decade witnessed sarcasm detection to become a highly phenomenal topic of research; however the task of automated detection of sarcastic comments in a text remains an elusive problem. Sarcasm detection has eventually become a considerably significant task in the domain of sentiment classification. Without properly detecting the sarcasm from textual comments, sentiment classification remains incomplete and may lead to wrongful conclusion and decision. In this paper, we present a recurrent neural network (RNN)-based bidirectional long-short term memory (Bi-LSTM) network for sarcasm detection.The proposed technique has been applied to a combined dataset which is produced form news headline sarcasm dataset and news headline sarcasm version 2. Results of our technique renders enhanced performance over the existing technique found in literature.

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Khan, A., DIPANKAR MAJUMDAR, BIKROMADITTYA MONDAL, & SOUMEN MUKHERJEE. (2022). A DEEP LEARNING APPROACH TO SARCASM DETECTION FROM COMPOSITE TEXTUAL DATA. INFOCOMP Journal of Computer Science, 21(2). Retrieved from https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/2266
Machine Learning and Computational Intelligence


Abduljabbar, R. L., Dia, H., and Tsai, P.-W.

Unidirectional and bidirectional lstm models for

short-term traffic prediction. Journal of Advanced

Transportation, 2021, 2021.

Alharbi, N. M., Alghamdi, N. S., Alkhammash,

E. H., and Al Amri, J. F. Evaluation of sentiment

analysis via word embedding and rnn variants for

amazon online reviews. Mathematical Problems

in Engineering, 2021, 2021.

Amir, S., Wallace, B. C., Lyu, H., and Silva, P. C.

M. J. Modelling context with user embeddings for

sarcasm detection in social media. arXiv preprint

arXiv:1607.00976, 2016.

Bouazizi, M. and Ohtsuki, T. O. A pattern-based

approach for sarcasm detection on twitter. IEEE

Access, 4:5477–5488, 2016.

Castro, S., Hazarika, D., Pérez-Rosas, V., Zim-

mermann, R., Mihalcea, R., and Poria, S. Towards

multimodal sarcasm detection (an _obviously_

perfect paper). arXiv preprint arXiv:1906.01815,

Dhola, K. and Saradva, M. A comparative eval-

uation of traditional machine learning and deep

learning classification techniques for sentiment

analysis. In 2021 11th International Conference

on Cloud Computing, Data Science & Engineer-

ing (Confluence), pages 932–936. IEEE, 2021.

Ghosh, A. and Veale, T. Fracking sarcasm using

neural network. In Proceedings of the 7th work-

shop on computational approaches to subjectivity,

sentiment and social media analysis, pages 161–

, 2016.

Ghosh, D. and Muresan, S. " with 1 follower i

must be awesome: P." exploring the role of irony

markers in irony recognition. In Twelfth Interna-

tional AAAI Conference on Web and Social Media,

Goldberg, Y. Neural network methods for natural

language processing. Synthesis lectures on human

language technologies, 10(1):1–309, 2017.

González-Ibánez, R., Muresan, S., and Wa-

cholder, N. Identifying sarcasm in twitter: a closer

look. In Proceedings of the 49th Annual Meeting

of the Association for Computational Linguistics:

Human Language Technologies, pages 581–586,

Gupta, S., Singh, R., and Singla, V. Emoticon

and text sarcasm detection in sentiment analy-

sis. In First International Conference on Sustain-

able Technologies for Computational Intelligence,

pages 1–10. Springer, 2020.

Han, H., Liu, J., and Liu, G. Attention-based

memory network for text sentiment classification.

IEEE Access, 6:68302–68310, 2018.

Hazarika, D., Poria, S., Gorantla, S., Cambria,

E., Zimmermann, R., and Mihalcea, R. Cascade:

Contextual sarcasm detection in online discussion

forums. arXiv preprint arXiv:1805.06413, 2018.

Jurafsky, D. and Martin, J. H. Speech and lan-

guage processing (draft). preparation [cited 2020

June 1] Available from: https://web. stanford.

edu/˜ jurafsky/slp3, 2018.

Kreuz, R. J. and Glucksberg, S. How to be

sarcastic: The echoic reminder theory of verbalirony. Journal of experimental psychology: Gen-

eral, 118(4):374, 1989.

Kulkarni, S. and Biswas, A. Sarcasm detection

methods in deep learning: Literature review. ICT

Analysis and Applications, pages 507–512, 2020.

Liu, B. Sentiment analysis: Mining opinions,

sentiments, and emotions. Cambridge university

press, 2020.

Lukin, S. and Walker, M. Really? well. appar-

ently bootstrapping improves the performance of

sarcasm and nastiness classifiers for online dia-

logue. arXiv preprint arXiv:1708.08572, 2017.

Mahajan, D. and Chaudhary, D. K. Sentiment

analysis using rnn and google translator. In 2018

th International Conference on Cloud Comput-

ing, Data Science & Engineering (Confluence),

pages 798–802. IEEE, 2018.

Manning, C. and Schutze, H. Foundations of sta-

tistical natural language processing. MIT press,

Misra, R. and Arora, P. Sarcasm detection

using hybrid neural network. arXiv preprint

arXiv:1908.07414, 2019.

Monika, R., Deivalakshmi, S., and Janet, B. Senti-

ment analysis of us airlines tweets using lstm/rnn.

In 2019 IEEE 9th International Conference on Ad-

vanced Computing (IACC), pages 92–95. IEEE,

Muresan, S., Gonzalez-Ibanez, R., Ghosh, D., and

Wacholder, N. Identification of nonliteral lan-

guage in social media: A case study on sarcasm.

Journal of the Association for Information Science

and Technology, 67(11):2725–2737, 2016.

Ni, R. and Cao, H. Sentiment analysis based on

glove and lstm-gru. In 2020 39th Chinese Con-

trol Conference (CCC), pages 7492–7497. IEEE,

Riloff, E., Qadir, A., Surve, P., De Silva, L.,

Gilbert, N., and Huang, R. Sarcasm as contrast

between a positive sentiment and negative situa-

tion. In Proceedings of the 2013 conference on

empirical methods in natural language process-

ing, pages 704–714, 2013.

Saha, B. N., Senapati, A., and Mahajan, A. Lstm

based deep rnn architecture for election sentiment

analysis from bengali newspaper. In 2020 Interna-

tional Conference on Computational Performance

Evaluation (ComPE), pages 564–569, 2020.

Sriram, B., Fuhry, D., Demir, E., Ferhatosman-

oglu, H., and Demirbas, M. Short text classifica-

tion in twitter to improve information filtering. In

Proceedings of the 33rd international ACM SIGIR

conference on Research and development in infor-

mation retrieval, pages 841–842, 2010.

Thakur, S., Singh, S., and Singh, M. Detect-

ing sarcasm in text. In International Conference

on Intelligent Systems Design and Applications,

pages 996–1005. Springer, 2018.

Tsur, O., Davidov, D., and Rappoport, A. Icws-

mâa great catchy name: Semi-supervised recog-

nition of sarcastic sentences in online product re-

views. In fourth international AAAI conference on

weblogs and social media, 2010.

Tungthamthiti, P., Shirai, K., and Mohd, M.

Recognition of sarcasms in tweets based on con-

cept level sentiment analysis and supervised learn-

ing approaches. In Proceedings of the 28th Pa-

cific Asia conference on language, information

and computing, pages 404–413, 2014.

Usama, M., Ahmad, B., Song, E., Hossain, M. S.,

Alrashoud, M., and Muhammad, G. Attention-

based sentiment analysis using convolutional and

recurrent neural network. Future Generation

Computer Systems, 113:571–578, 2020.

Pelser, D. and Murrell, H. Deep and dense sar-

casm detection. arXiv preprint arXiv:1911.07474,

[36] Vimali, J. and Murugan, S. A text based sentiment

analysis model using bi-directional lstm networks.

In 2021 6th International Conference on Commu-

nication and Electronics Systems (ICCES), pages

–1658. IEEE, 2021.

Rajadesingan, A., Zafarani, R., and Liu, H. Sar-

casm detection on twitter: A behavioral model-

ing approach. In Proceedings of the eighth ACM

international conference on web search and data

mining, pages 97–106, 2015. [37] Wang, Z., Wu, Z., Wang, R., and Ren, Y.

Twitter sarcasm detection exploiting a context-

based model. In international conference on web

information systems engineering, pages 77–91.

Springer, 2015.[38] Wen, S., Wei, H., Yang, Y., Guo, Z., Zeng,

Z., Huang, T., and Chen, Y. Memristive lstm

network for sentiment analysis. IEEE Transac-

tions on Systems, Man, and Cybernetics: Systems,

(3):1794–1804, 2019.

Yaghoobian, H., Arabnia, H. R., and Rasheed, K.

Sarcasm detection: A comparative study. CoRR,

abs/2107.02276, 2021.

Yenter, A. and Verma, A. Deep cnn-lstm with

combined kernels from multiple branches for

imdb review sentiment analysis. In 2017 IEEE 8th

Annual Ubiquitous Computing, Electronics and

Mobile Communication Conference (UEMCON),

pages 540–546. IEEE, 2017.

Young, H. Young t., hazarika d., poria s., cambria

e. Recent trends in deep learning based natural

language processing, IEEE Computational Intel-

ligence magazine, 13(3):55–75, 2018.

Zarrella, D. The social media marketing book. "

O’Reilly Media, Inc.", 2009.

Zhang, D., Tian, L., Hong, M., Han, F., Ren,

Y., and Chen, Y. Combining convolution neu-

ral network and bidirectional gated recurrent unit

for sentence semantic classification. IEEE access,

:73750–73759, 2018.

Zhang, M., Zhang, Y., and Fu, G. Tweet sar-

casm detection using deep neural network. In Pro-

ceedings of COLING 2016, the 26th International

Conference on Computational Linguistics: tech-

nical papers, pages 2449–2460, 2016.

Zhao, J., Liu, K., and Xu, L. Sentiment analysis:

mining opinions, sentiments, and emotions, 2016.

Zou, H., Wu, Y., Zhang, H., and Zhan, Y.

Short-term traffic flow prediction based on pcc-

bilstm. In 2020 International Conference on

Computer Engineering and Application (ICCEA),

pages 489–493. IEEE, 2020.