Arabic CyberBullying Detection Using Arabic Sentiment Analysis

Abstract

The Sentiment Analysis is used for the text analysing, detecting opinion, and classification of the text attitude. It becomes quite challenging when it is applied to the Arabic language due to the structural and morphological complexity, known as "Arabic Sentiment Analysis (ASA)." For the implementation of ASA, 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. Use internet technology to bully a person by using aggressive and offensive words is known asCyberBullying. 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 is not 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 standard "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.

Word embedding for detecting cyberbullying based on recurrent neural networks

“…Three different pre-trained GloVe corpora (the Arabic pre-trained corpus GloVe of 256 D, pre-trained corpus GloVe, which contains multilanguages with 50 D, 100 D and 200 D, and An English pre-trained corpus GloVe of 100D). The best results for the Arabic cyberbullying dataset were achieved using the GloVe of 256 D and GRU classifier applied to the original dataset, which was 87.83% compared with [12], which reached an accuracy of 84.03%. While the best result for the English cyberbullying datasets was 93.38% achieved when using GloVe 100 D and GRU classifier after the normalization process.…”

“…The first dataset is the Arabic cyberbullying dataset. The size of the first dataset is 17,748 Arabic tweets, including 14,178 cyberbullying tweets and 3,570 non-cyberbullying tweets [12]. The second dataset is the English cyberbullying dataset.…”

Arabic Cyberbullying Detection Using Support Vector Machine with Cuckoo Search

“…The long-short-term memory (LSTM) deep learning model outperforms other classical cyberbullying classifiers, with an accuracy of 72%. The researchers in [9] used tools such as AraBully Keywords to collect data from Twitter for their work. The total number of tweets was 17748, and the researcher used SVM in WEKA and the Python compilation tool to work in the compilation phase after preprocessing.…”

“…The dataset was obtained from [9], where the tweets written in the Arabic language were stored in "Comma Separated Value" (CSV). The words that are mostly used to do Arabic cyberbullying include words like "ugliness," "racial discrimination," "tententiousness," "intolerance of opinion," and "dynasty."…”

The Development of Sentiment Analysis from a linguistic perspective

“…it used ensemble technique that utilized SVM, Logistic Regression Classifiers Naïve Bayes. Additionally, [13] conducted a study using Arabic dataset in order to detect cyberbullying using sentiment analysis techniques and machine learning methods.…”