Research Article
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Sarcasm Detection in Online Social Networks Using Machine Learning Methods

Year 2022, Volume: 3 Issue: 1, 42 - 53, 30.06.2022

Harun Bingol Muhammed Yıldırım

https://doi.org/10.46572/naturengs.1100358

Abstract

Our lives have completely changed since the Internet came into our lives. Role models for people are not only the people around them but people all over the world. Although there are positive aspects of this situation, we will deal with the negative aspects in this study. One of these negative aspects is that people share their ideas on social networks without any supervision. In this way, people who use social networks are told offensive words by people they do not know in real life. Sometimes these words are not directly insulting, but they are expressed sarcastically and annoy the interlocutor. In this study, the detection of sarcastic words in social networks is considered a classification problem. Since the data type used in the proposed method is text-based, both text mining and machine learning methods are used together. In this study, the sarcastic word classification process was carried out using a data set obtained from the Twitter social network, which includes two public classes. The performance of the proposed method was obtained with the Random Forest algorithm with an accuracy of 94.9%.

Keywords

Social networks Sarcasm detection Text mining Classification.

References

  • [1] Baloglu, U. B., Alatas, B., & Bingol, H. (2019, November). Assessment of Supervised Learning Algorithms for Irony Detection in Online Social Media. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-5). IEEE.
  • [2] Joshi, A., Bhattacharyya, P., & Carman, M. J. (2017). Automatic sarcasm detection: A survey. ACM Computing Surveys (CSUR), 50(5), 1-22.
  • [3] Access Date: 06/01/2022, https://www.kaggle.com/theynalzada/news-headlines-for-sarcasm detection ?select =Data.csv
  • [4] Bingol, H., & Alatas, B. (2019, November). Rumor Detection in Social Media using machine learning methods. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-4). IEEE.
  • [5] Campbell, J. D., & Katz, A. N. (2012). Are there necessary conditions for inducing a sense of sarcastic irony?. Discourse Processes, 49(6), 459-480.
  • [6] Joshi, A., Sharma, V., & Bhattacharyya, P. (2015, July). Harnessing context incongruity for sarcasm detection. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (pp. 757-762).
  • [7] Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., & Huang, R. (2013, October). Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 704-714).
  • [8] Ghosh, D., Fabbri, A. R., & Muresan, S. (2017). The role of conversation context for sarcasm detection in online interactions. arXiv preprint arXiv:1707.06226.
  • [9] Mishra, A., Kanojia, D., Nagar, S., Dey, K., & Bhattacharyya, P. (2017). Harnessing cognitive features for sarcasm detection. arXiv preprint arXiv:1701.05574..
  • [10] A. Mullen, L., Benoit, K., Keyes, O., Selivanov, D., & Arnold, J. (2018). Fast, consistent tokenization of natural language text. Journal of Open Source Software, 3(23), 655.
  • [11] Ozbay, F. A., & Alatas, B. (2020). Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Statistical Mechanics and its Applications, 540, 123174.
  • [12] Azam, N., & Yao, J. (2012). Comparison of term frequency and document frequency based feature selection metrics in text categorization. Expert Systems with Applications, 39(5), 4760-4768.
  • [13] Fitri, S. (2014). Perbandingan Kinerja Algoritma Klasifikasi Naïve Bayesian, Lazy-Ibk, Zero-R, Dan Decision Tree-J48. Data Manajemen dan Teknologi Informasi (DASI), 15(1), 33.
  • [14] Lewis, D. D. (1998, April). Naive (Bayes) at forty: The independence assumption in information retrieval. In European conference on machine learning (pp. 4-15). Springer, Berlin, Heidelberg.
  • [15] Menard, S. (2002). Applied logistic regression analysis (Vol. 106). Sage.
  • [16] Pal, M. (2005). Random forest classifier for remote sensing classification. International journal of remote sensing, 26(1), 217-222.
  • [17] Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), 119-139.
  • [18] Schapire, R. E. (2013). Explaining adaboost. In Empirical inference (pp. 37-52). Springer, Berlin, Heidelberg.
  • [19] Baydoğan, V. C., & Alataş, B. (2021). Çevrimiçi Sosyal Ağlarda Nefret Söylemi Tespiti için Yapay Zeka Temelli Algoritmaların Performans Değerlendirmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 33(2), 745-754.
  • [20] Ozbay, F. A., & Alatas, B. (2019). A novel approach for detection of fake news on social media using metaheuristic optimization algorithms. Elektronika ir Elektrotechnika, 25(4), 62-67.

Year 2022, Volume: 3 Issue: 1, 42 - 53, 30.06.2022

Harun Bingol Muhammed Yıldırım

https://doi.org/10.46572/naturengs.1100358

Abstract

References

  • [1] Baloglu, U. B., Alatas, B., & Bingol, H. (2019, November). Assessment of Supervised Learning Algorithms for Irony Detection in Online Social Media. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-5). IEEE.
  • [2] Joshi, A., Bhattacharyya, P., & Carman, M. J. (2017). Automatic sarcasm detection: A survey. ACM Computing Surveys (CSUR), 50(5), 1-22.
  • [3] Access Date: 06/01/2022, https://www.kaggle.com/theynalzada/news-headlines-for-sarcasm detection ?select =Data.csv
  • [4] Bingol, H., & Alatas, B. (2019, November). Rumor Detection in Social Media using machine learning methods. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-4). IEEE.
  • [5] Campbell, J. D., & Katz, A. N. (2012). Are there necessary conditions for inducing a sense of sarcastic irony?. Discourse Processes, 49(6), 459-480.
  • [6] Joshi, A., Sharma, V., & Bhattacharyya, P. (2015, July). Harnessing context incongruity for sarcasm detection. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (pp. 757-762).
  • [7] Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., & Huang, R. (2013, October). Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 704-714).
  • [8] Ghosh, D., Fabbri, A. R., & Muresan, S. (2017). The role of conversation context for sarcasm detection in online interactions. arXiv preprint arXiv:1707.06226.
  • [9] Mishra, A., Kanojia, D., Nagar, S., Dey, K., & Bhattacharyya, P. (2017). Harnessing cognitive features for sarcasm detection. arXiv preprint arXiv:1701.05574..
  • [10] A. Mullen, L., Benoit, K., Keyes, O., Selivanov, D., & Arnold, J. (2018). Fast, consistent tokenization of natural language text. Journal of Open Source Software, 3(23), 655.
  • [11] Ozbay, F. A., & Alatas, B. (2020). Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Statistical Mechanics and its Applications, 540, 123174.
  • [12] Azam, N., & Yao, J. (2012). Comparison of term frequency and document frequency based feature selection metrics in text categorization. Expert Systems with Applications, 39(5), 4760-4768.
  • [13] Fitri, S. (2014). Perbandingan Kinerja Algoritma Klasifikasi Naïve Bayesian, Lazy-Ibk, Zero-R, Dan Decision Tree-J48. Data Manajemen dan Teknologi Informasi (DASI), 15(1), 33.
  • [14] Lewis, D. D. (1998, April). Naive (Bayes) at forty: The independence assumption in information retrieval. In European conference on machine learning (pp. 4-15). Springer, Berlin, Heidelberg.
  • [15] Menard, S. (2002). Applied logistic regression analysis (Vol. 106). Sage.
  • [16] Pal, M. (2005). Random forest classifier for remote sensing classification. International journal of remote sensing, 26(1), 217-222.
  • [17] Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), 119-139.
  • [18] Schapire, R. E. (2013). Explaining adaboost. In Empirical inference (pp. 37-52). Springer, Berlin, Heidelberg.
  • [19] Baydoğan, V. C., & Alataş, B. (2021). Çevrimiçi Sosyal Ağlarda Nefret Söylemi Tespiti için Yapay Zeka Temelli Algoritmaların Performans Değerlendirmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 33(2), 745-754.
  • [20] Ozbay, F. A., & Alatas, B. (2019). A novel approach for detection of fake news on social media using metaheuristic optimization algorithms. Elektronika ir Elektrotechnika, 25(4), 62-67.
There are 20 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Harun Bingol 0000-0001-5071-4616

Muhammed Yıldırım 0000-0003-1866-4721

Publication Date June 30, 2022
Submission Date April 8, 2022
Acceptance Date May 13, 2022
Published in Issue Year 2022 Volume: 3 Issue: 1

Cite

APA Bingol, H., & Yıldırım, M. (2022). Sarcasm Detection in Online Social Networks Using Machine Learning Methods. NATURENGS, 3(1), 42-53. https://doi.org/10.46572/naturengs.1100358
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