Systematic review of sentiment analysis and predict sarcastic
DOI:
https://doi.org/10.29304/jqcm.2023.15.2.1241Keywords:
NLP, sentiment analysis, feature extraction, machine learning, RNNAbstract
With the rise of social media on the web, sentiment analysis has become one of the most important areas of study. Today, millions of people share their thoughts, ideas, feelings, and opinions on social media sites like Twitter and Facebook. Sentiment analysis, also called opinion mining, is mostly about classifying and predicting how people feel about a certain target. It involves putting text documents or sentences into groups based on how positive or negative they are about a certain topic. Researchers always find "natural language processing" to be one of the most interesting topics. To solve different problems and improve the accuracy of different applications, it is always helpful to know the exact meaning of what is being said in a conversation. Sentiment analysis uses natural language processing (NLP) and learning models like machine learning and deep learning algorithms to figure out how people feel about the data given. Sentiment analysis looks at sarcasm because sarcasm is a way for people to say how they feel about something without saying it directly. People means the exact opposite of what the sentence says at first glance. Sarcasm is hard to figure out because each sarcastic sentence is different. This paper will talk about what has been done in the field of sarcasm detection, the different techniques used, and the problems that still need to be solved.
Downloads
Download data is not yet available.
References
[1] A. Alsaeedi and M. Z. Khan, “A Study on Sentiment Analysis Techniques of Twitter Data Efficient data dissemination protocols for IoT applications View project Sentiment Analysis of social media data View project A Study on Sentiment Analysis Techniques of Twitter Data,” Article in International Journal of Advanced Computer Science and Applications, vol. 10, no. 2, 2019, doi: 10.14569/IJACSA.2019.0100248.
[2] M. Singh, A. K. Jakhar, and S. Pandey, “Sentiment analysis on the impact of coronavirus in social life using the BERT model,” Soc Netw Anal Min, vol. 11, no. 1, Dec. 2021, doi: 10.1007/s13278-021-00737-z.
[3] M. Sykora, S. Elayan, and T. W. Jackson, “A qualitative analysis of sarcasm, irony and related #hashtags on Twitter,” Big Data Soc, vol. 7, no. 2, 2020, doi: 10.1177/2053951720972735.
[4] L. Ren, B. Xu, H. Lin, X. Liu, and L. Yang, “Sarcasm Detection with Sentiment Semantics Enhanced Multi-level Memory Network,” Neurocomputing, vol. 401, pp. 320–326, Aug. 2020, doi: 10.1016/J.NEUCOM.2020.03.081.
[5] J. Žižka, F. Dařena, and A. Svoboda, Text mining with machine learning: principles and techniques. CRC Press, 2019.
[6] L. Araujo, F. Lopez-Ostenero, J. Martinez-Romo, and L. Plaza, “Deep-Learning Approach to Educational Text Mining and Application to the Analysis of Topics’ Difficulty,” IEEE Access, vol. 8, pp. 218002–218014, 2020, doi: 10.1109/ACCESS.2020.3042099.
[7] E. Kouloumpis, T. Wilson, and J. Moore, “Twitter Sentiment Analysis: The Good the Bad and the OMG!,” Proceedings of the International AAAI Conference on Web and Social Media, vol. 5, no. 1, pp. 538–541, Aug. 2011, doi: 10.1609/ICWSM.V5I1.14185.
[8] A. Shelar and C. Y. Huang, “Sentiment analysis of twitter data,” Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, pp. 1301–1302, Dec. 2018, doi: 10.1109/CSCI46756.2018.00252.
[9] V. S. Pagolu, K. N. Reddy, G. Panda, and B. Majhi, “Sentiment analysis of Twitter data for predicting stock market movements,” International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings, pp. 1345–1350, Jun. 2017, doi: 10.1109/SCOPES.2016.7955659.
[10] H. T. Duong and T. A. Nguyen-Thi, “A review: preprocessing techniques and data augmentation for sentiment analysis,” Comput Soc Netw, vol. 8, no. 1, pp. 1–16, Dec. 2021, doi: 10.1186/S40649-020-00080-X/FIGURES/4.
[11] F. M. Javed Mehedi Shamrat et al., “Sentiment analysis on twitter tweets about COVID-19 vaccines using NLP and supervised KNN classification algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 23, no. 1, pp. 463–470, 2021, doi: 10.11591/ijeecs.v23.i1.pp463-470.
[12] V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment Analysis of Social Media Twitter with Case of Anti-LGBT Campaign in Indonesia using Naïve Bayes, Decision Tree, and Random Forest Algorithm,” Procedia Comput Sci, vol. 161, pp. 765–772, Jan. 2019, doi: 10.1016/J.PROCS.2019.11.181.
[13] M. Qorib, T. Oladunni, M. Denis, E. Ososanya, and P. Cotae, “Covid-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset,” Expert Syst Appl, vol. 212, p. 118715, Feb. 2023, doi: 10.1016/J.ESWA.2022.118715.
[14] Anees, Ansari Fatima, Arsalaan Shaikh, Arbaz Shaikh, and Sufiyan Shaikh. ‘Survey paper on sentiment analysis: Techniques and challenges.’ EasyChair2516-2314 (2020).”.
[15] R. Jamil, I. Ashraf, F. Rustam, E. Saad, A. Mehmood, and G. S. Choi, “Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model,” PeerJ Comput Sci, vol. 7, p. e645, Aug. 2021, doi: 10.7717/peerj-cs.645.
[16] S. Fransiska, R. Rianto, and A. I. Gufroni, “Sentiment Analysis Provider By.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method,” Scientific Journal of Informatics, vol. 7, no. 2, pp. 203–212, Nov. 2020, doi: 10.15294/SJI.V7I2.25596.
[17] I. Yahav, O. Shehory, and D. Schwartz, “Comments Mining With TF-IDF: The Inherent Bias and Its Removal,” IEEE Trans Knowl Data Eng, vol. 31, no. 3, pp. 437–450, Mar. 2019, doi: 10.1109/TKDE.2018.2840127.
[18] “Güner, Levent, Emilie Coyne, and Jim Smit. ‘Sentiment analysis for Amazon. com reviews.’ Big Data in Media Technology (DM2583) KTH Royal Institute of Technology, Stockholm (2019).”.
[19] K. L. Kohsasih, B. H. Hayadi, Robet, C. Juliandy, O. Pribadi, and Andi, “Sentiment Analysis for Financial News Using RNN-LSTM Network,” 2022 4th International Conference on Cybernetics and Intelligent System, ICORIS 2022, 2022, doi: 10.1109/ICORIS56080.2022.10031595.
[20] M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artif Intell Rev, vol. 55, no. 7, pp. 5731–5780, Oct. 2022, doi: 10.1007/s10462-022-10144-1.
[21] H. Kang, S. J. Yoo, and D. Han, “Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews,” Expert Syst Appl, vol. 39, no. 5, pp. 6000–6010, Apr. 2012, doi: 10.1016/J.ESWA.2011.11.107.
[22] V. Kumar and B. Subba, “A tfidfvectorizer and SVM based sentiment analysis framework for text data corpus,” 26th National Conference on Communications, NCC 2020, Feb. 2020, doi: 10.1109/NCC48643.2020.9056085.
[23] Y.-M. Li and T.-Y. Li, “Deriving market intelligence from microblogs,” Decis Support Syst, vol. 55, pp. 206–217, 2013, doi: 10.1016/j.dss.2013.01.023.
[24] C. C. Chen and Y. De Tseng, “Quality evaluation of product reviews using an information quality framework,” Decis Support Syst, vol. 50, no. 4, pp. 755–768, Mar. 2011, doi: 10.1016/J.DSS.2010.08.023.
[25] H. Hasanli and S. Rustamov, “Sentiment Analysis of Azerbaijani twits Using Logistic Regression, Naive Bayes and SVM,” 13th IEEE International Conference on Application of Information and Communication Technologies, AICT 2019 - Proceedings, Oct. 2019, doi: 10.1109/AICT47866.2019.8981793.
[26] A. Antim, Application of ML in Text Analysis: Sentiment Analysis of Twitter Data Using Logistic Regression. 2023.
[27] H. Guo, “Comparison Of Neural Network and Traditional Classifiers for Twitter Sentiment Analysis,” Highlights in Science, Engineering and Technology, vol. 38, pp. 1062–1070, Mar. 2023, doi: 10.54097/HSET.V38I.5996.
[28] Sartini, Subiyanto, and M. M. F. Alim, “A sentiment analysis study for twitter using the various model of convolutional neural network,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jun. 2021. doi: 10.1088/1742-6596/1918/4/042136.
[29] R. Ni and H. Cao, “Sentiment Analysis based on GloVe and LSTM-GRU,” Chinese Control Conference, CCC, vol. 2020-July, pp. 7492–7497, Jul. 2020, doi: 10.23919/CCC50068.2020.9188578.
[30] R. Wang, Z. Li, J. Cao, T. Chen, and L. Wang, “Convolutional Recurrent Neural Networks for Text Classification,” Proceedings of the International Joint Conference on Neural Networks, vol. 2019-July, Jul. 2019, doi: 10.1109/IJCNN.2019.8852406.
[31] AlSalman, Hussain. ‘An improved approach for sentiment analysis of arabic tweets in twitter social media.’ In 2020 3rd International conference on computer applications & information security (ICCAIS), pp. 1-4. IEEE, 2020.”.
[32] Alzyout, Moath, Emran AL Bashabsheh, Hassan Najadat, and Ahmad Alaiad. ‘Sentiment Analysis of Arabic Tweets about Violence Against Women using Machine Learning.’ In 2021 12th International Conference on Information and Communication Systems (ICICS), pp. 171-176. IEEE, 2021.”.
[33] S. S. Salim, A. Nidhi Ghanshyam, D. M. Ashok, D. Burhanuddin Mazahir, and B. S. Thakare, “Deep LSTM-RNN with Word Embedding for Sarcasm Detection on Twitter,” 2020 International Conference for Emerging Technology (INCET), Jun. 2020, doi: 10.1109/INCET49848.2020.9154162.
[34] M. W. Habib and Z. N. Sultani, “Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques,” Journal of Science ANJS, vol. 24, no. 3, pp. 50–54, 2021, doi: 10.22401/ANJS.24.3.08.
[35] “Rachman, Fika Hastarita. ‘Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regresion.’ In 2020 6th Information Technology International Seminar (ITIS), pp. 238-242. IEEE, 2020.”.
[36] G. Kamil and E. B. Setiawan, “Aspect-Level Sentiment Analysis on Social Media Using Gated Recurrent Unit (GRU),” Building of Informatics, Technology and Science (BITS), vol. 4, no. 4, Mar. 2023, doi: 10.47065/BITS.V4I4.3105.
[37] M. S. Hossen, A. H. Jony, T. Tabassum, M. T. Islam, M. M. Rahman, and T. Khatun, “Hotel review analysis for the prediction of business using deep learning approach,” Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, pp. 1489–1494, Mar. 2021, doi: 10.1109/ICAIS50930.2021.9395757.
[38] Y. G. Jung, K. T. Kim, B. Lee, and H. Y. Youn, “Enhanced Naive Bayes Classifier for real-Time sentiment analysis with SparkR,” 2016 International Conference on Information and Communication Technology Convergence, ICTC 2016, pp. 141–146, Nov. 2016, doi: 10.1109/ICTC.2016.7763455.
[39] V. Tyagi, A. Kumar, and S. Das, “Sentiment Analysis on Twitter Data Using Deep Learning approach,” Proceedings - IEEE 2020 2nd International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020, pp. 187–190, Dec. 2020, doi: 10.1109/ICACCCN51052.2020.9362853.
[40] A. I. Saad, “Opinion Mining on US Airline Twitter Data Using Machine Learning Techniques,” 16th International Computer Engineering Conference, ICENCO 2020, pp. 59–63, Dec. 2020, doi: 10.1109/ICENCO49778.2020.9357390.
[41] Dharmavarapu, Bala Durga, and Jayanag Bayana, ‘Sarcasm detection in Twitter using sentiment analysis.’ International Journal of Recent Technology and Engineering (IJRTE) 8, no. 1 (2018).”.
[42] “Irony and Sarcasm - Roger Kreuz - Google Books.” https://books.google.iq/books?hl=en&lr=&id=z2PMDwAAQBAJ&oi=fnd&pg=PP7&dq=features+for+sarcasm+detection+(irony,+exaggeration,+and+juxtaposition)+sentiment+analysis&ots=Sl-dct4BK0&sig=yJDUVkExgvV6n8JWTKASsLa0Bsg&redir_esc=y#v=onepage&q&f=false (accessed May 20, 2023).
[43] R. Ortega-Bueno, P. Rosso, and J. E. Medina Pagola, “Multi-view informed attention-based model for Irony and Satire detection in Spanish variants,” Knowl Based Syst, vol. 235, p. 107597, Jan. 2022, doi: 10.1016/J.KNOSYS.2021.107597.
[44] Y. Y. Tan, C. O. Chow, J. Kanesan, J. H. Chuah, and Y. L. Lim, “Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning,” Wirel Pers Commun, vol. 129, no. 3, p. 2213, Apr. 2023, doi: 10.1007/S11277-023-10235-4.
[45] A. Khare, A. Gangwar, and S. Singh, ‘Sentiment analysis and sarcasm detection of indian general election tweets.’ arXiv preprint arXiv:2201.02127 (2022).”.
[46] E. Barbieri, F. Barbieri, F. Ronzano, and H. Saggion, “Procesamiento del Lenguaje Natural Sociedad Española para el Procesamiento del Lenguaje Natural Is This Tweet Satirical? A Computational Approach for Satire Detection in Spanish *,” pp. 135–142, 2015, [Online]. Available: http://www.redalyc.org/articulo.oa?id=515751524015
[47] Y. Yunitasari, A. Musdholifah, and A. K. Sari, “Sarcasm Detection For Sentiment Analysis in Indonesian Tweets,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 13, no. 1, p. 53, Jan. 2019, doi: 10.22146/ijccs.41136.
[2] M. Singh, A. K. Jakhar, and S. Pandey, “Sentiment analysis on the impact of coronavirus in social life using the BERT model,” Soc Netw Anal Min, vol. 11, no. 1, Dec. 2021, doi: 10.1007/s13278-021-00737-z.
[3] M. Sykora, S. Elayan, and T. W. Jackson, “A qualitative analysis of sarcasm, irony and related #hashtags on Twitter,” Big Data Soc, vol. 7, no. 2, 2020, doi: 10.1177/2053951720972735.
[4] L. Ren, B. Xu, H. Lin, X. Liu, and L. Yang, “Sarcasm Detection with Sentiment Semantics Enhanced Multi-level Memory Network,” Neurocomputing, vol. 401, pp. 320–326, Aug. 2020, doi: 10.1016/J.NEUCOM.2020.03.081.
[5] J. Žižka, F. Dařena, and A. Svoboda, Text mining with machine learning: principles and techniques. CRC Press, 2019.
[6] L. Araujo, F. Lopez-Ostenero, J. Martinez-Romo, and L. Plaza, “Deep-Learning Approach to Educational Text Mining and Application to the Analysis of Topics’ Difficulty,” IEEE Access, vol. 8, pp. 218002–218014, 2020, doi: 10.1109/ACCESS.2020.3042099.
[7] E. Kouloumpis, T. Wilson, and J. Moore, “Twitter Sentiment Analysis: The Good the Bad and the OMG!,” Proceedings of the International AAAI Conference on Web and Social Media, vol. 5, no. 1, pp. 538–541, Aug. 2011, doi: 10.1609/ICWSM.V5I1.14185.
[8] A. Shelar and C. Y. Huang, “Sentiment analysis of twitter data,” Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, pp. 1301–1302, Dec. 2018, doi: 10.1109/CSCI46756.2018.00252.
[9] V. S. Pagolu, K. N. Reddy, G. Panda, and B. Majhi, “Sentiment analysis of Twitter data for predicting stock market movements,” International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings, pp. 1345–1350, Jun. 2017, doi: 10.1109/SCOPES.2016.7955659.
[10] H. T. Duong and T. A. Nguyen-Thi, “A review: preprocessing techniques and data augmentation for sentiment analysis,” Comput Soc Netw, vol. 8, no. 1, pp. 1–16, Dec. 2021, doi: 10.1186/S40649-020-00080-X/FIGURES/4.
[11] F. M. Javed Mehedi Shamrat et al., “Sentiment analysis on twitter tweets about COVID-19 vaccines using NLP and supervised KNN classification algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 23, no. 1, pp. 463–470, 2021, doi: 10.11591/ijeecs.v23.i1.pp463-470.
[12] V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment Analysis of Social Media Twitter with Case of Anti-LGBT Campaign in Indonesia using Naïve Bayes, Decision Tree, and Random Forest Algorithm,” Procedia Comput Sci, vol. 161, pp. 765–772, Jan. 2019, doi: 10.1016/J.PROCS.2019.11.181.
[13] M. Qorib, T. Oladunni, M. Denis, E. Ososanya, and P. Cotae, “Covid-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset,” Expert Syst Appl, vol. 212, p. 118715, Feb. 2023, doi: 10.1016/J.ESWA.2022.118715.
[14] Anees, Ansari Fatima, Arsalaan Shaikh, Arbaz Shaikh, and Sufiyan Shaikh. ‘Survey paper on sentiment analysis: Techniques and challenges.’ EasyChair2516-2314 (2020).”.
[15] R. Jamil, I. Ashraf, F. Rustam, E. Saad, A. Mehmood, and G. S. Choi, “Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model,” PeerJ Comput Sci, vol. 7, p. e645, Aug. 2021, doi: 10.7717/peerj-cs.645.
[16] S. Fransiska, R. Rianto, and A. I. Gufroni, “Sentiment Analysis Provider By.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method,” Scientific Journal of Informatics, vol. 7, no. 2, pp. 203–212, Nov. 2020, doi: 10.15294/SJI.V7I2.25596.
[17] I. Yahav, O. Shehory, and D. Schwartz, “Comments Mining With TF-IDF: The Inherent Bias and Its Removal,” IEEE Trans Knowl Data Eng, vol. 31, no. 3, pp. 437–450, Mar. 2019, doi: 10.1109/TKDE.2018.2840127.
[18] “Güner, Levent, Emilie Coyne, and Jim Smit. ‘Sentiment analysis for Amazon. com reviews.’ Big Data in Media Technology (DM2583) KTH Royal Institute of Technology, Stockholm (2019).”.
[19] K. L. Kohsasih, B. H. Hayadi, Robet, C. Juliandy, O. Pribadi, and Andi, “Sentiment Analysis for Financial News Using RNN-LSTM Network,” 2022 4th International Conference on Cybernetics and Intelligent System, ICORIS 2022, 2022, doi: 10.1109/ICORIS56080.2022.10031595.
[20] M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artif Intell Rev, vol. 55, no. 7, pp. 5731–5780, Oct. 2022, doi: 10.1007/s10462-022-10144-1.
[21] H. Kang, S. J. Yoo, and D. Han, “Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews,” Expert Syst Appl, vol. 39, no. 5, pp. 6000–6010, Apr. 2012, doi: 10.1016/J.ESWA.2011.11.107.
[22] V. Kumar and B. Subba, “A tfidfvectorizer and SVM based sentiment analysis framework for text data corpus,” 26th National Conference on Communications, NCC 2020, Feb. 2020, doi: 10.1109/NCC48643.2020.9056085.
[23] Y.-M. Li and T.-Y. Li, “Deriving market intelligence from microblogs,” Decis Support Syst, vol. 55, pp. 206–217, 2013, doi: 10.1016/j.dss.2013.01.023.
[24] C. C. Chen and Y. De Tseng, “Quality evaluation of product reviews using an information quality framework,” Decis Support Syst, vol. 50, no. 4, pp. 755–768, Mar. 2011, doi: 10.1016/J.DSS.2010.08.023.
[25] H. Hasanli and S. Rustamov, “Sentiment Analysis of Azerbaijani twits Using Logistic Regression, Naive Bayes and SVM,” 13th IEEE International Conference on Application of Information and Communication Technologies, AICT 2019 - Proceedings, Oct. 2019, doi: 10.1109/AICT47866.2019.8981793.
[26] A. Antim, Application of ML in Text Analysis: Sentiment Analysis of Twitter Data Using Logistic Regression. 2023.
[27] H. Guo, “Comparison Of Neural Network and Traditional Classifiers for Twitter Sentiment Analysis,” Highlights in Science, Engineering and Technology, vol. 38, pp. 1062–1070, Mar. 2023, doi: 10.54097/HSET.V38I.5996.
[28] Sartini, Subiyanto, and M. M. F. Alim, “A sentiment analysis study for twitter using the various model of convolutional neural network,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jun. 2021. doi: 10.1088/1742-6596/1918/4/042136.
[29] R. Ni and H. Cao, “Sentiment Analysis based on GloVe and LSTM-GRU,” Chinese Control Conference, CCC, vol. 2020-July, pp. 7492–7497, Jul. 2020, doi: 10.23919/CCC50068.2020.9188578.
[30] R. Wang, Z. Li, J. Cao, T. Chen, and L. Wang, “Convolutional Recurrent Neural Networks for Text Classification,” Proceedings of the International Joint Conference on Neural Networks, vol. 2019-July, Jul. 2019, doi: 10.1109/IJCNN.2019.8852406.
[31] AlSalman, Hussain. ‘An improved approach for sentiment analysis of arabic tweets in twitter social media.’ In 2020 3rd International conference on computer applications & information security (ICCAIS), pp. 1-4. IEEE, 2020.”.
[32] Alzyout, Moath, Emran AL Bashabsheh, Hassan Najadat, and Ahmad Alaiad. ‘Sentiment Analysis of Arabic Tweets about Violence Against Women using Machine Learning.’ In 2021 12th International Conference on Information and Communication Systems (ICICS), pp. 171-176. IEEE, 2021.”.
[33] S. S. Salim, A. Nidhi Ghanshyam, D. M. Ashok, D. Burhanuddin Mazahir, and B. S. Thakare, “Deep LSTM-RNN with Word Embedding for Sarcasm Detection on Twitter,” 2020 International Conference for Emerging Technology (INCET), Jun. 2020, doi: 10.1109/INCET49848.2020.9154162.
[34] M. W. Habib and Z. N. Sultani, “Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques,” Journal of Science ANJS, vol. 24, no. 3, pp. 50–54, 2021, doi: 10.22401/ANJS.24.3.08.
[35] “Rachman, Fika Hastarita. ‘Twitter sentiment analysis of Covid-19 using term weighting TF-IDF and logistic regresion.’ In 2020 6th Information Technology International Seminar (ITIS), pp. 238-242. IEEE, 2020.”.
[36] G. Kamil and E. B. Setiawan, “Aspect-Level Sentiment Analysis on Social Media Using Gated Recurrent Unit (GRU),” Building of Informatics, Technology and Science (BITS), vol. 4, no. 4, Mar. 2023, doi: 10.47065/BITS.V4I4.3105.
[37] M. S. Hossen, A. H. Jony, T. Tabassum, M. T. Islam, M. M. Rahman, and T. Khatun, “Hotel review analysis for the prediction of business using deep learning approach,” Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, pp. 1489–1494, Mar. 2021, doi: 10.1109/ICAIS50930.2021.9395757.
[38] Y. G. Jung, K. T. Kim, B. Lee, and H. Y. Youn, “Enhanced Naive Bayes Classifier for real-Time sentiment analysis with SparkR,” 2016 International Conference on Information and Communication Technology Convergence, ICTC 2016, pp. 141–146, Nov. 2016, doi: 10.1109/ICTC.2016.7763455.
[39] V. Tyagi, A. Kumar, and S. Das, “Sentiment Analysis on Twitter Data Using Deep Learning approach,” Proceedings - IEEE 2020 2nd International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020, pp. 187–190, Dec. 2020, doi: 10.1109/ICACCCN51052.2020.9362853.
[40] A. I. Saad, “Opinion Mining on US Airline Twitter Data Using Machine Learning Techniques,” 16th International Computer Engineering Conference, ICENCO 2020, pp. 59–63, Dec. 2020, doi: 10.1109/ICENCO49778.2020.9357390.
[41] Dharmavarapu, Bala Durga, and Jayanag Bayana, ‘Sarcasm detection in Twitter using sentiment analysis.’ International Journal of Recent Technology and Engineering (IJRTE) 8, no. 1 (2018).”.
[42] “Irony and Sarcasm - Roger Kreuz - Google Books.” https://books.google.iq/books?hl=en&lr=&id=z2PMDwAAQBAJ&oi=fnd&pg=PP7&dq=features+for+sarcasm+detection+(irony,+exaggeration,+and+juxtaposition)+sentiment+analysis&ots=Sl-dct4BK0&sig=yJDUVkExgvV6n8JWTKASsLa0Bsg&redir_esc=y#v=onepage&q&f=false (accessed May 20, 2023).
[43] R. Ortega-Bueno, P. Rosso, and J. E. Medina Pagola, “Multi-view informed attention-based model for Irony and Satire detection in Spanish variants,” Knowl Based Syst, vol. 235, p. 107597, Jan. 2022, doi: 10.1016/J.KNOSYS.2021.107597.
[44] Y. Y. Tan, C. O. Chow, J. Kanesan, J. H. Chuah, and Y. L. Lim, “Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning,” Wirel Pers Commun, vol. 129, no. 3, p. 2213, Apr. 2023, doi: 10.1007/S11277-023-10235-4.
[45] A. Khare, A. Gangwar, and S. Singh, ‘Sentiment analysis and sarcasm detection of indian general election tweets.’ arXiv preprint arXiv:2201.02127 (2022).”.
[46] E. Barbieri, F. Barbieri, F. Ronzano, and H. Saggion, “Procesamiento del Lenguaje Natural Sociedad Española para el Procesamiento del Lenguaje Natural Is This Tweet Satirical? A Computational Approach for Satire Detection in Spanish *,” pp. 135–142, 2015, [Online]. Available: http://www.redalyc.org/articulo.oa?id=515751524015
[47] Y. Yunitasari, A. Musdholifah, and A. K. Sari, “Sarcasm Detection For Sentiment Analysis in Indonesian Tweets,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 13, no. 1, p. 53, Jan. 2019, doi: 10.22146/ijccs.41136.
Downloads
Published
2023-09-24
How to Cite
Kzar, B. I., & Safi, H. H. (2023). Systematic review of sentiment analysis and predict sarcastic. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(2), Comp Page 166–179. https://doi.org/10.29304/jqcm.2023.15.2.1241
Issue
Section
Computer Articles
