Almutiry, S., Abdel Fattah, M. (2021). Arabic CyberBullying Detection Using Arabic Sentiment Analysis. The Egyptian Journal of Language Engineering, 8(1), 39-50. doi: 10.21608/ejle.2021.50240.1017
Samar Almutiry; Mohamed Abdel Fattah. "Arabic CyberBullying Detection Using Arabic Sentiment Analysis". The Egyptian Journal of Language Engineering, 8, 1, 2021, 39-50. doi: 10.21608/ejle.2021.50240.1017
Almutiry, S., Abdel Fattah, M. (2021). 'Arabic CyberBullying Detection Using Arabic Sentiment Analysis', The Egyptian Journal of Language Engineering, 8(1), pp. 39-50. doi: 10.21608/ejle.2021.50240.1017
Almutiry, S., Abdel Fattah, M. Arabic CyberBullying Detection Using Arabic Sentiment Analysis. The Egyptian Journal of Language Engineering, 2021; 8(1): 39-50. doi: 10.21608/ejle.2021.50240.1017
Arabic CyberBullying Detection Using Arabic Sentiment Analysis
1College of Computer Science and Engineering, Taibah University
Saudi Arabia- Almadinah Almunawarah
2Department of Electronics Technology, FIE, Helwan University, Cairo, Egypt
Abstract
Abstract: The Sentiment Analysis is used for the text analysing, and classification of the text attitude.We are using the computing advancement in the form of Machine Learning (ML) and Support Vector Machine (SVM) algorithm to train a dataset which is collected automatically through ArabiTools and Twitter API. The dataset contents are labelled by both means, automatic and manual, in order to maintain the efficiency of the detection of CyberBullying tweets. The dataset is automatically labelled with respect to the nature of the tweet. If a tweet contains one or more CyberBullying words, it is labelled as CyberBullying, while if there isn’t any word with aggressive meaning found, it is marked as the NonCyberBullying. After the data collection, there are several pre-processing techniques utilized, including the Normalization, Tokenization, Light Stemmer, ArabicStemmerKhoja, and Term Frequency-Inverse Document Frequency (TF-IDF)” term weighting schema.” After the preliminary steps, (SVM), a “supervised algorithm,” is used with WEKA and Python. There are three experiments that take place one with the WEKA tool using the Light Stemmer, the other is again with WEKA using ArabicStemmerKhoja, and the final experiment was performed with Python. The results are showing the WEKA is more efficient in classifying the text correctly, while Python is more effective with time to build the model. WEKA using the Light Stemmer have the efficiency of 85.49% and taken 352.51 seconds, and the WEKA using ArabicStemmerKhoja have the efficiency of 85.38% and taken 212.12 seconds, while the Python have the efficiency of 84.03% and taken 142.68 seconds
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