International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 16, No. 4, 8 Aug. 2024
Cover page and Table of Contents: PDF (size: 708KB)
Detecting Sarcasm Text in Sentiment Analysis Using Hybrid Machine Learning Approach
PDF (708KB), PP.72-85
Views: 0 Downloads: 0
Author(s)
Index Terms
Sentiment Analysis, Sarcastic People, Machine Learning, Sarcasm Text
Abstract
It's getting harder for 21st-century citizens to effectively detect sarcasm using sentiment analysis in a world full of sarcastic people and identifying sarcasm aids in understanding the unpleasant truth hidden beneath polite language. While sarcasm in text is frequently identified, very little research has been done on text sarcasm recognition in memes. This study uses a hybrid machine learning strategy to increase accuracy in identifying sarcasm text in sentiment analysis. It also compares the hybrid approach to existing approaches, like Random Forest, Logistic Regression, Naive Bayes, Stochastic Gradient Descent, and Decision Tree. The effectiveness of several methods is assessed in this study using recall, precision, and f-measure. The results showed that the suggested strategy (0.8004%) received the highest score when the prediction accuracy of several machine learning approaches was compared. The proposed hybrid approach performs much better in terms of enhancing accuracy.
Cite This Paper
Neha Singh, Umesh Chandra Jaiswal, Ritu Singh, "Detecting Sarcasm Text in Sentiment Analysis Using Hybrid Machine Learning Approach", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.4, pp.72-85, 2024. DOI:10.5815/ijisa.2024.04.05
Reference
[1]Verma, P., Shukla, N. and Shukla, A.P., 2021, March. Techniques of sarcasm detection: A review. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 968-972). IEEE.
[2]Salim, S.S., Ghanshyam, A.N., Ashok, D.M., Mazahir, D.B. and Thakare, B.S., 2020, June. Deep LSTM-RNN with word embedding for sarcasm detection on Twitter. In 2020 international conference for emerging technology (INCET) (pp. 1-4). IEEE.
[3]Ahuja, R., Bansal, S., Prakash, S., Venkataraman, K. and Banga, A., 2018. Comparative study of different sarcasm detection algorithms based on behavioral approach. Procedia computer science, 143, pp.411-418.
[4]Jena, A.K., Sinha, A. and Agarwal, R., 2020, July. C-net: Contextual network for sarcasm detection. In Proceedings of the second workshop on figurative language processing (pp. 61-66).
[5]Poria, S., Cambria, E., Hazarika, D. and Vij, P., 2016. A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv preprint arXiv:1610.08815.
[6]Chatterjee, N., Aggarwal, T. and Maheshwari, R., 2020. Sarcasm detection using deep learning-based techniques. In Deep Learning-Based Approaches for Sentiment Analysis (pp. 237-258). Springer, Singapore.
[7]Bouazizi, M. and Ohtsuki, T.O., 2016. A pattern-based approach for sarcasm detection on twitter. IEEE Access, 4, pp.5477-5488.
[8]Ashok, D.M., Ghanshyam, A.N., Salim, S.S., Mazahir, D.B. and Thakare, B.S., 2020, June. Sarcasm detection using genetic optimization on LSTM with CNN. In 2020 International Conference for Emerging Technology (INCET) (pp. 1-4). IEEE.
[9]Sundararajan, K. and Palanisamy, A., 2020. Multi-rule based ensemble feature selection model for sarcasm type detection in twitter. Computational intelligence and neuroscience, 2020.
[10]Hazarika, D., Poria, S., Gorantla, S., Cambria, E., Zimmermann, R. and Mihalcea, R., 2018. Cascade: Contextual sarcasm detection in online discussion forums. arXiv preprint arXiv:1805.06413.
[11]Javdan, S. and Minaei-Bidgoli, B., 2020, July. Applying transformers and aspect-based sentiment analysis approaches on sarcasm detection. In Proceedings of the second workshop on figurative language processing (pp. 67-71).
[12]Potamias, R.A., Siolas, G. and Stafylopatis, A.G., 2020. A transformer-based approach to irony and sarcasm detection. Neural Computing and Applications, 32(23), pp.17309-17320.
[13]Ren, L., Xu, B., Lin, H., Liu, X. and Yang, L., 2020. Sarcasm detection with sentiment semantics enhanced multi-level memory network. Neurocomputing, 401, pp.320-326.
[14]Kumar, A., Sangwan, S.R., Arora, A., Nayyar, A. and Abdel-Basset, M., 2019. Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE access, 7, pp.23319-23328.
[15]Malave, N. and Dhage, S.N., 2020. Sarcasm detection on twitter: user behavior approach. In Intelligent Systems, Technologies and Applications (pp. 65-76). Springer, Singapore.
[16]Eke, C.I., Norman, A., Shuib, L., Fatokun, F.B. and Omame, I., 2020, March. The significance of global vectors representation in sarcasm analysis. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS) (pp. 1-7). IEEE.
[17]Gupta, R., Kumar, J. and Agrawal, H., 2020, May. A statistical approach for sarcasm detection using Twitter data. In 2020 4th international conference on intelligent computing and control systems (ICICCS) (pp. 633-638). IEEE.
[18]Nimala, K., Jebakumar, R. and Saravanan, M., 2021. Sentiment topic sarcasm mixture model to distinguish sarcasm prevalent topics based on the sentiment bearing words in the tweets. Journal of Ambient Intelligence and Humanized Computing, 12(6), pp.6801-6810.
[19]Pawar, N. and Bhingarkar, S., 2020, June. Machine learning based sarcasm detection on twitter data. In 2020 5th international conference on communication and electronics systems (ICCES) (pp. 957-961). IEEE.
[20]Kumar, A., Narapareddy, V.T., Srikanth, V.A., Malapati, A. and Neti, L.B.M., 2020. Sarcasm detection using multi-head attention based bidirectional LSTM. Ieee Access, 8, pp.6388-6397.
[21]Onan, A. and Toçoğlu, M.A., 2021. A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification. IEEE Access, 9, pp.7701-7722.
[22]Liu, P., Chen, W., Ou, G., Wang, T., Yang, D. and Lei, K., 2014, June. Sarcasm detection in social media based on imbalanced classification. In International Conference on Web-Age Information Management (pp. 459-471). Springer, Cham.
[23]Amir, S., Wallace, B.C., Lyu, H. and Silva, P.C.M.J., 2016. Modelling context with user embeddings for sarcasm detection in social media. arXiv preprint arXiv:1607.00976.
[24]Meriem, A.B., Hlaoua, L. and Romdhane, L.B., 2021. A fuzzy approach for sarcasm detection in social networks. Procedia Computer Science, 192, pp.602-611.
[25]Suhaimin, M.S.M., Hijazi, M.H.A., Alfred, R. and Coenen, F., 2017, May. Natural language processing based features for sarcasm detection: An investigation using bilingual social media texts. In 2017 8th International conference on information technology (ICIT) (pp. 703-709). IEEE.
[26]Vinoth, D. and Prabhavathy, P., 2022. An intelligent machine learning-based sarcasm detection and classification model on social networks. The Journal of Supercomputing, 78(8), pp.10575-10594.
[27]Calderon, F.H., Kuo, P.C., Yen-Hao, H. and Chen, Y.S., 2019. Emotion Combination in Social Media Comments as Features for Sarcasm Detection. In The International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM).
[28]Dridi, A. and Reforgiato Recupero, D., 2019. Leveraging semantics for sentiment polarity detection in social media. International Journal of Machine Learning and Cybernetics, 10(8), pp.2045-2055.
[29]Sinha, A., Patekar, P. and Mamidi, R., 2019, December. Unsupervised approach for monitoring satire on social media. In Proceedings of the 11th Forum for Information Retrieval Evaluation (pp. 36-41).
[30]Bharti, S.K., Vachha, B., Pradhan, R.K., Babu, K.S. and Jena, S.K., 2016. Sarcastic sentiment detection in tweets streamed in real time: a big data approach. Digital Communications and Networks, 2(3), pp.108-121.
[31]Bharti, S.K., Naidu, R. and Babu, K.S., 2017, December. Hyperbolic feature-based sarcasm detection in tweets: a machine learning approach. In 2017 14th IEEE India council international conference (INDICON) (pp. 1-6). IEEE.
[32]Khatri, A., 2020. Sarcasm detection in tweets with BERT and GloVe embeddings. arXiv preprint arXiv:2006.11512.
[33]Garg, A. and Duhan, N., 2020. Sarcasm Detection on Twitter Data Using Support Vector Machine. ICTACT Journal of soft computing, 10(4), pp.2165-2170.
[34]Bharti, S.K., Babu, K.S. and Jena, S.K., 2015, August. Parsing-based sarcasm sentiment recognition in twitter data. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 1373-1380). IEEE.
[35]Rahayu, D.A., Kuntur, S. and Hayatin, N., 2018, October. Sarcasm detection on Indonesian twitter feeds. In 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 137-141). IEEE.
[36]Bouazizi, M. and Ohtsuki, T., 2015, December. Sarcasm detection in twitter:" all your products are incredibly amazing!!!"-are they really?. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
[37]Haripriya, V., Patil, P.G. and Anil Kumar, T.V., 2021. Sarcasm Detection on Twitter Data Using R and Python. In Advances in Medical Physics and Healthcare Engineering (pp. 455-462). Springer, Singapore.
[38]Sreelakshmi, K. and Rafeeque, P.C., 2018, July. An effective approach for detection of sarcasm in tweets. In 2018 International CET Conference on Control, Communication, and Computing (IC4) (pp. 377-382). IEEE.
[39]Handoyo, A.T. and Suhartono, D., 2021. Sarcasm Detection in Twitter--Performance Impact while using Data Augmentation: Word Embeddings. arXiv preprint arXiv:2108.09924.
[40]Misra, R. and Arora, P., 2019. Sarcasm detection using hybrid neural network. arXiv preprint arXiv:1908.07414.
[41]Mandal, P.K. and 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.
[42]Liu, L., Priestley, J.L., Zhou, Y., Ray, H.E. and Han, M., 2019, December. 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.
[43]Li, L., Levi, O., Hosseini, P. and Broniatowski, D.A., 2020. A multi-modal method for satire detection using textual and visual cues. arXiv preprint arXiv:2010.06671.
[44]Rameshbhai, C.J. and Paulose, J., 2019. Opinion mining on newspaper headlines using SVM and NLP. International journal of electrical and computer engineering (IJECE), 9(3), pp.2152-2163.
[45]Agarwal, A., Sharma, V., Sikka, G. and Dhir, R., 2016, March. Opinion mining of news headlines using SentiWordNet. In 2016 Symposium on Colossal Data Analysis and Networking (CDAN) (pp. 1-5). IEEE.
[46]Nazeer, I., Rashid, M., Gupta, S.K. and Kumar, A., 2021. Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis. In Analyzing Global Social Media Consumption (pp. 16-28). IGI Global.
[47]Ahmad, M., Aftab, S., Muhammad, S.S. and Ahmad, S., 2017. Machine learning techniques for sentiment analysis: A review. Int. J. Multidiscip. Sci. Eng, 8(3), p.27.
[48]Chavan, V.S. and Shylaja, S.S., 2015, August. Machine learning approach for detection of cyber-aggressive comments by peers on social media network. In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 2354-2358). IEEE.
[49]Bayhaqy, A., Sfenrianto, S., Nainggolan, K. and Kaburuan, E.R., 2018, October. Sentiment analysis about E-commerce from tweets using decision tree, K-nearest neighbor, and naïve bayes. In 2018 international conference on orange technologies (ICOT) (pp. 1-6). IEEE.
[50]Panwar, S.S., Negi, P.S., Panwar, L.S. and Raiwani, Y., 2019. Implementation of machine learning algorithms on CICIDS-2017 dataset for intrusion detection using WEKA. International Journal of Recent Technology and Engineering Regular Issue, 8(3), pp.2195-2207.
[51]Bharti, S.K., Pradhan, R., Babu, K.S. and Jena, S.K., 2017. Sarcasm analysis on twitter data using machine learning approaches. Trends in social network analysis: Information propagation, user behavior modeling, forecasting, and vulnerability assessment, pp.51-76.
[52]Gul, S., Khan, R.U., Ullah, M., Aftab, R., Waheed, A. and Wu, T.Y., 2022. Tanz‐Indicator: A Novel Framework for Detection of Perso‐Arabic‐Scripted Urdu Sarcastic Opinions. Wireless Communications and Mobile Computing, 2022(1), p.9151890.
[53]Pelser, D. and Murrell, H., 2019. Deep and dense sarcasm detection. arXiv preprint arXiv:1911.07474.