Sentiment Analysis of Arabic Tweets about Violence Against Women using Machine Learning
Keywords:Sentiment analysis, Support Vector Machines, Na¨ıve Bayes, K-nearest-neighbor, Deep Learning, Decision Trees, SA, SVM, NB, KNN, DL, DT
Social Media platforms, such as Twitter become a significant pulse of smart societies that are shaping our communities by sensing people’s information and perceptions across living areas over space and time. Social media sentiment analysis helps in recognizing people’s emotions and attitudes and assessing various public issues, for example, women’s rights and violence against women. In this paper, we use the sentence based sentiment analysis to study the topic of women’s rights. We collected dialect Arabic tweets from the whole Arab world as data via a Twitter API, and we clean it to use in the classification step. We have examined different types of traditional classification algorithms namely, Support Vector Machine, K-Nearest-Neighbour, Decision Trees, and Naıve Bayes, then compared these results with deep learning results. Finally, we compared the classification results using the precision, recall, and accuracy measurements and we found the Support Vector Machine algorithm gains the best results, while the Naıve Baye was the worst. We also noticed that there is increasing attention to women’s rights in the Arab world
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Copyright (c) 2021 Hassan Najadat, Dr., Moath Mohmmad Zyout, Emran AL Bashabsheh
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