Browsing by Author "Fatima Salih Ibrahim"

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    A Design of a Model for Analyzing Political Sudanese Arabic Tweets Using Deep Learning
    (ِِAl-neelain University, 2021) Fatima Salih Ibrahim
    Sentiment analysis of the Arabic language has gained the attention of many researchers because of the increasing number of Arabic internet users, and the exponential growth of Arabic content online. Sentiment detection of Arabic tweets is at interesting research topic and it enables scholars to analyze huge resources of shared opinions in social media websites such as Facebook and 1 weeter. Sentiment analysis has many applications ranging from financial analysis to political decision-making domains. It is one of the more complex natural language processing tasks due to the informal noisy contents and the rich morphology of Arabic language. Many studies have been investigated for Arabic sentiment analysis. However, most of these works ignore analyzing political Arabic tweets, specifically the Sudanese Arabic dialect. Ehis thesis, focus t: analyz _ Arabic tweets during Sudanese revolution 2018 and developed a deep leaming model for classifying the tweets. The ;w:=;~ "~»cvf:~:»-.?=.~’»~' is a deep learning-based approach. This approach employed word embedding with convolutional neural network and long short-tenn memory network techniques to represent the tweets and extract the feature vectors. Afier that, the feature vectors \\\-to fed to the classifier to detect the sentiments. This approach exploited Word2Vec, convolutional neural networks and recurrent neural networks algorithms. The proposed model has three steps namely data preprocessing, feature extraction, and classification phase. The model has a network architecture to represent the tweets and improve the classification performance. \\. < -_‘.\._1'Li . -»>