Implementation Of Convolutional Neural Network (CNN) To Detect Hate Speech And Emotions On Twitter
##plugins.themes.bootstrap3.article.main##
Abstract
The research aims to develop an accurate and efficient hate speech detection model on Twitter's social media platform by leveraging the power of the Convolutional Neural Network. (CNN). The focus of this research is on identifying hate speeches that are loaded with negative sentiment, especially those related to racial, religious, and sexual orientation issues in the context of the Indonesian language. The research process involved collecting relevant Twitter datasets, preprocessing text to clear and compile data, and word representation using Word2Vec to capture contextual meanings. Specifically designed CNN models are then trained on that dataset. CNN's advantages in automatically extracting semantic features from text, coupled with the use of Word2Vec, allow the model to have high accuracy, which is 87%-99% for emotional assessment and 99% for hate speech assessment. This makes the model very effective in detecting subtle patterns in language that indicate the presence of hate speech. This research has made a significant contribution to the development of a better content moderation system on social media. With its ability to detect hate speech in real time, the model can help create a safer and more inclusive online environment. However, this research still has some limitations, such as limited data set size and variations of hate speech that are not fully represented. Therefore, further research is needed to overcome these limitations and improve the performance of the model.
##plugins.themes.bootstrap3.article.details##
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The writer agreed that the article copyright by Smatika journal and the writer has the right to disseminate the paper published without permission in advance.
[2] R. Hayami, S. Mohnica, and Soni, “Klasifikasi multilabel komentar toxic pada sosial media twitter menggunakan convolutional neural network(CNN),” Jurnal CoSciTech (Computer Science and Information Technology), vol. 4, no. 1, pp. 1–6, Apr. 2023, doi: 10.37859/coscitech.v4i1.4365.
[3] A. G. D’Sa, I. Illina, and D. Fohr, “BERT and fastText Embeddings for Automatic Detection of Toxic Speech,” in 2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA), IEEE, Feb. 2020, pp. 1–5. doi: 10.1109/OCTA49274.2020.9151853.
[4] A. B. Syahputri and Y. Sibaroni, “Comparative Analysis of CNN and LSTM Performance for Hate Speech Detection on Twitter,” in 2023 11th International Conference on Information and Communication Technology (ICoICT), IEEE, Aug. 2023, pp. 190–195. doi: 10.1109/ICoICT58202.2023.10262656.
[5] Syafruddin, A. Thaba, and R. Ananda, “UJARAN KEBENCIAN NETIZEN INDONESIA PADA AKUN TWITTER ES TEH: TINJAUAN LINGUISTIK FORENSIK,” Semantik, vol. 13, no. 1, pp. 15–28, Feb. 2024, doi: 10.22460/semantik.v13i1.p15-28.
[6] S. Riyadi, A. D. Andriyani, A. M. Masyhur, C. Damarjati, and M. I. Solihin, “Detection of Indonesian Hate Speech on Twitter Using Hybrid CNN-RNN,” in 2023 International Conference on Information Technology and Computing (ICITCOM), IEEE, Dec. 2023, pp. 352–356. doi: 10.1109/ICITCOM60176.2023.10442041.
[7] Oryza Habibie Rahman, Gunawan Abdillah, and Agus Komarudin, “Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 17–23, Feb. 2021, doi: 10.29207/resti.v5i1.2700.
[8] D. S. Ashari, B. Irawan, and C. Setianingsih, “Sentiment Analysis on Online Transportation Services Using Convolutional Neural Network Method,” in 2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), IEEE, Oct. 2021, pp. 335–340. doi: 10.23919/EECSI53397.2021.9624261.
[9] A. P. J. Dwitama and S. Hidayat, “Identifikasi Ujaran Kebencian Multilabel Pada Teks Twitter Berbahasa Indonesia Menggunakan Convolution Neural Network,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 3, no. 2, p. 117, Dec. 2021, doi: 10.30865/json.v3i2.3610.
[10] F. D. Ananda and Y. Pristyanto, “Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan Algoritma Support Vector Machine,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 2, pp. 407–416, May 2021, doi: 10.30812/matrik.v20i2.1130.
[11] D. C. Rahmadani, S. Khomsah, and M. Y. Fathoni, “Analisis Emosi Wisatawan Menggunakan Metode Lexicon Text Analysis,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 10, no. 1, May 2024, doi: 10.28932/jutisi.v10i1.6690.
[12] D. V. Bhargav and D. R, “Performance Analysis of Logistic Regression Algorithm and Random Forest Algorithm for Predicting Product Review Analysis,” in 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), IEEE, Apr. 2024, pp. 1–5. doi: 10.1109/ICONSTEM60960.2024.10568774.
[13] A. A. Handoko, M. A. Rosid, and U. Indahyanti, “Implementasi Convolutional Neural Network (CNN) Untuk Pengenalan Tulisan Tangan Aksara Bima,” SMATIKA JURNAL, vol. 14, no. 01, pp. 96–110, Jul. 2024, doi: 10.32664/smatika.v14i01.1196.
[14] T. He, Y. Liu, Y. Yu, Q. Zhao, and Z. Hu, “Application of deep convolutional neural network on feature extraction and detection of wood defects,” Measurement, vol. 152, p. 107357, Feb. 2020, doi: 10.1016/j.measurement.2019.107357.
[15] L. Xiaolin, R. C. Panicker, B. Cardiff, and D. John, “Multistage Pruning of CNN Based ECG Classifiers for Edge Devices,” in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Nov. 2021, pp. 1965–1968. doi: 10.1109/EMBC46164.2021.9630588.
[16] M. Harahap, Em Manuel Laia, Lilis Suryani Sitanggang, Melda Sinaga, Daniel Franci Sihombing, and Amir Mahmud Husein, “Deteksi Penyakit Covid-19 Pada Citra X-Ray Dengan Pendekatan Convolutional Neural Network (CNN),” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 1, pp. 70–77, Feb. 2022, doi: 10.29207/resti.v6i1.3373.
[17] S. Hong, J. Kim, H.-G. Woo, Y.-C. Kim, and C. Lee, “Screening ideas in the early stages of technology development: A word2vec and convolutional neural network approach,” Technovation, vol. 112, p. 102407, Apr. 2022, doi: 10.1016/j.technovation.2021.102407.
[18] Merinda Lestandy and Abdurrahim, “Effect of Word2Vec Weighting with CNN-BiLSTM Model on Emotion Classification,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 12, no. 1, pp. 99–107, Mar. 2023, doi: 10.23887/janapati.v12i1.58571.
[19] D. Maheshwari, S. K. Ghosh, R. K. Tripathy, M. Sharma, and U. R. Acharya, “Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals,” Comput Biol Med, vol. 134, p. 104428, Jul. 2021, doi: 10.1016/j.compbiomed.2021.104428.
[20] V. A. Porter, B. A. Hobson, B. Foster, P. J. Lein, and A. J. Chaudhari, “Fully automated whole brain segmentation from rat MRI scans with a convolutional neural network,” J Neurosci Methods, vol. 405, p. 110078, May 2024, doi: 10.1016/j.jneumeth.2024.110078.