Lexicon-Based Indonesian Local Language Abusive Words Dictionary to Detect Hate Speech in Social Media
Downloads
Background: Hate speech is an expression to someone or a group of people that contain feelings of hate and/or anger at people or groups. On social media users are free to express themselves by writing harsh words and share them with a group of people so that it triggers separations and conflicts between groups. Currently, research has been conducted by several experts to detect hate speech in social media namely machine learning-based and lexicon-based, but the machine learning approach has a weakness namely the manual labelling process by an annotator in separating positive, negative or neutral opinions takes time long and tiring
Objective: This study aims to produce a dictionary containing abusive words from local languages in Indonesia. Lexicon-base is very dependent on the language contained in dictionary words. Indonesia has thousands of tribes with 2500 local languages, and 80% of the population of Indonesia use local languages in communication, with the result that a significant challenge to detect hate speech of social media.
Methods: Abusive words surveys are conducted by using proportionate stratified random sampling techniques in 4 major tribes on the island of Java, namely Betawi, Sundanese, Javanese, Madurese
Results: The experimental results produce 250 abusive words dictionary from 4 major Indonesian tribes to detect hate speech in Indonesian social media by using the lexicon-based approach.
Conclusion: A stratified random sampling technique has been conducted in 4 major Indonesian tribes to produce 250 abusive words for hate speech detection using the lexicon-based approach.
Z. Al and M. Amr, "Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach,” Computing, no. 0123456789, 2019.
M. Makrehchi, "The correlation between language shift and social conflicts in polarized social media,” Proc. - 2014 IEEE/WIC/ACM Int. Jt. Conf. Web Intell. Intell. Agent Technol. - Work. WI-IAT 2014, vol. 2, pp. 169–194, 2014.
Y. Rao, J. Lei, L. Wenyin, Q. Li, and M. Chen, "Building emotional dictionary for sentiment analysis of online news,” World Wide Web Internet Web Inf. Syst., pp. 723–742, 2014.
A. P. J. I. Indonesia, "Buletin APJII Edisi 22 Maret 2018,” Bul. APJII, 2018.
W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, Dec. 2014.
Kewarganegaraan, suku bangsa, Agama, dan bahasa sehari-hari Penduduk Indonesia. Hasil Sensus Penduduk 2010. Badan Pusat statistik, Jakarta-indonesia, 2011.
I. Alfina, R. Mulia, M. I. Fanany, and Y. Ekanata, "Hate speech detection in the Indonesian language: A dataset and preliminary study,” in 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017, 2018.
N. R. Fatahillah, P. Suryati, and C. Haryawan, "Implementation of Naive Bayes classifier algorithm on social media (Twitter) to the teaching of Indonesian hate speech,” in Proceedings - 2017 International Conference on Sustainable Information Engineering and Technology, SIET 2017, 2018.
M. O. Ibrohim and I. Budi, "A Dataset and Preliminaries Study for Abusive Language Detection in Indonesian Social Media,” Procedia
Comput. Sci., vol. 135, pp. 222–229, Jan. 2018.
M. O. Ibrohim and I. Budi, "Translated vs Non-Translated Method for Multilingual Hate Speech Identification in Twitter,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 4, pp. 1116–1123, 2019.
D. Alita, S. Priyanta, and N. Rokhman, "Analysis of Emoticon and Sarcasm Effect on Sentiment Analysis of Indonesian Language on Twitter,” J. Inf. Syst. Eng. Bus. Intell., vol. 5, no. 2, pp. 100, 2019.
F. Hemmatian and M. Karim, "A survey on classification techniques for opinion mining and sentiment analysis,” Artif. Intell. Rev., vol. 52, no. 3, pp. 1495–1545, 2019.
A. Jurek, M. D. Mulvenna, and Y. Bi, "Improved lexicon "‘ based sentiment analysis for social media analytics,” Secur. Inform., 2015.
N. D. T. Ruwandika and A. R. Weerasinghe, "Identification of Hate Speech in Social Media,” 2019.
T. L. Sutejo and D. P. Lestari, "Indonesia Hate Speech Detection Using Deep Learning,” in International Conference on Asian Language Processing (IALP), 2019, pp. 39–43.
N. Chetty and S. Alathur, "Hate speech review in the context of online social networks,” Aggress. Violent Behav., vol. 40, pp. 108–118, May 2018.
M. Wiegand, J. Ruppenhofer, A. Schmidt, and C. Greenberg, "Inducing a Lexicon of Abusive Words – a Feature-Based Approach,” Proc. 2018 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol., vol. 1, pp. 1046–1056, 2018.
H.-S. Lee, H.-R. Lee, J.-U. Park, and Y.-S. Han, "An abusive text detection system based on enhanced abusive and non-abusive word lists,” Decis. Support Syst., vol. 113, pp. 22–31, Sep. 2018.
I. B. Sumadyo, "Sekilas tentang Bentuk Umpatan dalam Bahasa Indonesia,” in 2 nd International Seminar on Quality and Affordable Education, 2013.
M. Ekawati, "Kesantunan Semu pada Tindak Tutur Ekspresif Marah dalam Bahasa Indonesia,” Adab. J. Bhs. dan Sastra, vol. 1, no. 1, pp. 1–22, 2018.
C. Bhadane, H. Dalal, and H. Doshi, "Sentiment Analysis: Measuring Opinions,” Procedia Comput. Sci., vol. 45, pp. 808–814, Jan. 2015.
A. W. Pradana and M. Hayaty, "The effect of stemming and removal of stopwords on the accuracy of sentiment analysis on indonesian-language texts,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, no. 3, 2019.
W. Kaur, V. Balakrishnan, O. Rana, and A. Sinniah, "Liking, sharing, commenting and reacting on Facebook: User behaviors' impact on sentiment intensity,” Telemat. Informatics, Dec. 2018.
S. Taj, B. B. Shaikh, and A. Fatemah Meghji, "Sentiment analysis of news articles: A lexicon based approach,” 2019 2nd Int. Conf. Comput. Math. Eng. Technol. iCoMET 2019, no. February, 2019.
N. D. Gitari, Z. Zuping, H. Damien, and J. Long, "A lexicon-based approach for hate speech detection,” Int. J. Multimed. Ubiquitous Eng., vol. 10, no. 4, pp. 215–230, 2015.
C. Teddlie and F. Yu, "Mixed Methods Sampling : A Typology Journal of Mixed Methods Research,” J. Mix. Methods Res., vol. 1, no. 1, pp. 77–100, 2007.
E. L. Slattery, C. C. J. Voelker, B. Nussenbaum, J. T. Rich, R. C. Paniello, and J. G. Neely, "A practical guide to surveys and questionnaires,” Otolaryngol. - Head Neck Surg., vol. 144, no. 6, pp. 831–837, 2011.
C. Fan, P. Zhang, and V. Sun, "Natural Disaster Information Statistics Study Based on Stratified Random Sampling Survey Statistical Methods,” in 2012 IEEE International Conference on Granular Computing Natural, 2012, vol. 00, pp. 1–4.
G. Sevilla, J. A. Ochave, T. G. Punsalan, B. P. Regala, and G. G. Uriarte, Research Methods. 2007.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
