Abstract
With time, people have graduated towards expressing themselves through the medium of the Internet, specifically over social media platforms. It creates scenarios where a user may, knowingly or unknowingly, make a reference or comment which may be derogatory to an individual and/or a section of society. It may hinder observant from participating in the conversation or even stop visiting the website altogether, thereby hurting the prospects of the website owner. A human may easily detect such infringements; however it is a huge pursuit for a computer. In this paper, we present a text classification method to classify the comments as insulting or otherwise. For this purpose, we extract features using various methods and enrich them using k-skip-n-grams to achieve a good set of features for the task. Further, feature selection is applied to obtain a subset of relevant features. Finally, a competitive and collaborative analysis of five different machine learning methods (classifiers) is presented to show that a collaborative model is a clear winner. It is a step towards making a machine learning based automated system to detect the insulting comments in the conversations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Chavan, V.S., Shylaja, S.S.: Machine learning approach for detection of cyber-aggressive comments by peers on social media network. In: Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2015)
Chikashi, N., Joel, T., Achint, T., Yashar, M.: Abusive language detection in online user content. In: Proceedings of International World Wide Web Conference (WWW), pp. 145–153 (2016)
Djuric, N., Zhou, J., Morris, R., Grbovic, M., Radosavljevic, V., Bhamidipati, N.: Hate speech detection with comment embeddings. In: Proceedings of International World Wide Web Conference (WWW), pp. 29–30 (2015)
Fernandez, J., Gutierrez, Y., Gomez, J.M., Martnez-Barco, P.: GPLSI: supervised sentiment analysis in Twitter using skipgrams. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval), pp. 294–299 (2014)
Guthrie, D., Allison, B., Liu, W., Guthrie, L., Wilks, Y.: A closer look at skip-gram modelling. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC-2006) (2006)
Kansara, B.K., Shekokar, N.M.: A framework for Cyberbullying detection in social network. Int. J. Current Eng. Technol. 5, 494–498 (2015)
Mahmud, A., Ahmed, K.Z., Khan, M.: Detecting flames and insults in text. In: Proceedings of the Sixth International Conference on Natural Language Processing (2008)
Maw, M., Vimala, B.: An analysis of hateful contents detection techniques on social media. Aust. J. Basic Appl. Sci. 10(3), 25–31 (2016)
Mohamed, M.B.I., Ouiem, B.: Insult detection in social network comments using possibilistic based fusion approach. In: Computer and Information Science. SCI, vol. 566, pp. 15–25 (2015)
Nahar, V., Al-Maskari, S., Li, X., Pang, C.: Semi-supervised learning for cyberbullying detection in social networks. In: Proceedings of Australian Database Conference. LNCS, vol. 8506, 160–171 (2014)
Razavi, A.H., Inkpen, D., Uritsky, S., Matwin, S.: Offensive language detection using multilevel classification. In: Proceedings of the 23rd Canadian Conference on Artificial Intelligence, pp. 16–27 (2010)
Sood, S.O., Churchill, E.F., Antin, J.: Automatic identification of personal insults on social news sites authors. J. Am. Soc. Inf. Sci. Technol. 63(2), 270–285 (2012). Table of Contents Archive
Xiang, G., Fan, B., Wang, L., Hong, J.I., Ros, C.P.: Detecting offensive tweets via topical feature discovery over a large scale twitter corpus. In: Proceedings of the 21st ACM Conference on Information and Knowledge Management (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Gupta, A., Singh, P.K. (2018). Detection of Insulting Comments in Online Discussion. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-76351-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76350-7
Online ISBN: 978-3-319-76351-4
eBook Packages: EngineeringEngineering (R0)